# Blockchain for Trust and Reputation Management in Cyber-physical Systems

Guntur Dharma Putra1,2, Volkan Dedeoglu3, Salil S Kanhere1,2 and Raja Jurdak4

(1UNSW Sydney 2Cyber Security CRC 3CSIRO Data61 4QUT Brisbane )

###### Abstract

The salient features of blockchain, such as decentralisation and transparency,
have allowed the development of Decentralised Trust and Reputation Management
Systems (DTRMS), which mainly aim to quantitatively assess the trustworthiness
of the network participants and help to protect the network from adversaries.
In the literature, proposals of DTRMS have been applied to various Cyber-
physical Systems (CPS) applications, including supply chains, smart cities and
distributed energy trading. In this article, we outline the building blocks of
a generic DTRMS and discuss how it can benefit from blockchain. To highlight
the significance of DTRMS, we present the state-of-the-art of DTRMS in various
field of CPS applications. In addition, we also outline challenges and future
directions in developing DTRMS for CPS.

##  1 Introduction

Trust is a subjective and intangible belief built up from consecutive
interactions with an entity or individual that perceives its behaviour [1]. As
it is linked to a specific behaviour or traits, trust is context-related and
thus cannot be generalised. According to Gambetta, trust is defined as a
subjective probability that an individual expects from another individual on
performing an expected action [2]. Occasionally, trust and reputation are
referred interchangeably in the literature. However, there is a subtle
difference between these two terms. Trust refers to a subjective belief
towards the behaviour of an entity that builds as more interactions happen,
while reputation can be seen as the aggregated opinion or trust degree of an
entity from other entities that have prior interaction with the entity.

Trust and Reputation Management Systems (TRMS) aim to assess the
accountability or trustworthiness of each participant in distributed systems
by means of a quantitative approach. In TRMS, trustworthiness is derived from
direct experience or recommendations from other peers and is represented as
numerical scores using which the trustworthiness level can be conveniently
measured. In general, the trust and reputation score can be used as a
safeguard to manage the associated risk in communicating with other peers in a
distributed system, which might be very dynamic and hostile.

There have been many applications of TRMS in Cyber-Physical Systems (CPS) and
Internet of Things (IoT). For example, TRMS is deployed in the context of
social IoT, which is used to assess the trustworthiness of each participating
node in the network [3]. In e-commerce sites, TRMS is implemented to help
customers determine the credibility of the sellers in the marketplace.

However, several challenges exist in building a TRMS. For instance,
traditional TRMS architectures rely on a centralised actor to manage the
collection of feedback and calculation of trust scores, which raises the risk
of data loss and manipulation by the centralised party. When the centralised
actor is compromised, an adversary may maliciously alter the trust computation
thus undermining the use of these metrics. In addition, authentication and
identification of users in TRMS may expose the actual identities of the users,
which should be concealed and protected.

Blockchain, the underpinning technology behind Bitcoin, has seen a lot of
interest, due to its inherent characteristics, such as traceability, tamper-
resilience, trustless environment, programmability, immutability and
transparency. These characteristics show promise in addressing the
aforementioned issues of TRMS. For instance, blockchain may replace the
trusted centralised actor that assesses the trustworthiness of participants in
traditional TRMS. The adoption of blockchain in TRMS is referred to in the
literature as Decentralised TRMS (DTRMS) [4]. While blockchain enables
trustless interaction between participants, a reputation system is still
required to provide some degree of trust quantification for off-chain
operations.

In this article, we present and discuss how blockchain technology can be
incorporated in TRMS for enhancing its effectiveness for CPS applications. We
outline the building blocks of a generic TRMS that delineate how trust is
empirically built up by collecting and aggregating evidence of direct and
indirect interactions to obtain a quantifiable trust measure. Then, we
describe the blockchain properties that can help address challenges in
building TRMS for CPS applications. The article also highlights the latest
developments of DTRMS for CPS by presenting some recent implementations of
DTRMS across various CPS application domains. We also outline the challenges
and future directions for DTRMS that still need to be addressed.

The rest of the article is organised as follows. Section 2 presents the
concept of DTRMS. Section 3 outlines several implementations of DTRMS for CPS.
We discuss the open challenges for future research directions in Section 4 and
give a conclusion of our article in Section 5.

##  2 Blockchain-based Trust and Reputation Management Systems

In this section, we discuss the necessity of a TRMS for CPS along with the
general properties of CPS applications, including trust derivation, types of
trust, evidence aggregation approach and trust dimensions. We also illustrate
the salient properties of blockchain that hold the potentials to enhance TRMS.

###  2.1 Trust and Reputation Management Systems for CPS

In general, CPS applications involve a group of agents collecting data from
physical environments and performing specific tasks based on the collected
data, which includes interactions with other agents in the network. While we
can assume that the majority of agents are honest, some agents may behave
opportunistically to maximise their gains through dishonest behaviour. In
addition, the collected data may also be noisy, faulty or maliciously
tampered. Ideally, an agent should not blindly trust other agents due to these
risks that may degrade the quality of service of their interaction. TRMS are
designed to quantitatively assess the trustworthiness of a particular agent or
data in a system through numerical and tangible values. In CPS, TRMS acts as
an intermediary between service providers and requesters by providing
protocols that guarantee trustworthiness in each interaction by means of
authentication, resource management and access control. We discuss the general
properties of a generic TRMS for CPS in this subsection.

####  2.1.1 Trust Derivation and Application

Similar to real life social interactions, computational trust is built
gradually from successive interactions between entities that correspond to
positive or negative experiences affecting the overall belief of the
trustworthiness level. In a generic TRMS, the interactions are assessed
empirically, which includes four steps for collecting and applying trust
computation, depicted in Fig. 1 [5].

Figure 1: The steps for trust derivation in a TRMS.

Information gathering

The first step in TRMS is defining the input parameters and attributes for
quantifying or computing the trustworthiness level, which in general is highly
application specific. Some of the examples include adherence to communication
protocol, quality of service and degree of satisfaction towards a service. The
TRMS should gather all of these input values either by 1) direct observations
or interactions, or 2) recommendation from other entities if prior
interactions are unavailable.

Trust Score Calculation

The next step includes the actual calculation of the trustworthiness level as
quantifiable values or scores according to the preferred trust or reputation
model. The TRMS may use various computation models that suit the application
requirements, for instance, statistical, game theory, fuzzy computation or
hybrid. Note that, the input attributes also determine the appropriate
computation model, e.g., sum and mean models are suitable for continuous input
values, while Bayesian model is more suited for discrete binary values [5]. In
addition, trust score calculation should also take into account the types of
trust (see Section 2.1.2).

Trust Propagation and Update

Typically, trust propagation can be performed in a centralised, distributed or
semi-distributed fashion, depending on the underlying architecture of the
system. The trust computation should be initiated based on temporal dynamics
depending on the specific application, which includes time-driven and event-
driven approaches. In the time-driven approach, the trust score is updated on
a regular basis, while the even-driven approach only requires updating the
trust values upon new interactions and events.

Trust Score Application

The specific manner in which the trust score is used depends on the
requirements and operation of the application. Generally the trust score is
employed to give certain quantified and fair measures for providing incentives
or enforcing penalties, which may include certain privileges and monetary
incentives or some restrictions and punishments. Section 3 discusses specific
examples of how the trust scores are manifested in various CPS applications.

####  2.1.2 Types of Trust

As discussed earlier, CPS applications rely on the data collected, processed
and transferred in the system, and the interactions among entities. Thus, we
can categorise the computation of trust as follows:

Behaviour-based Trust Computation

In behaviour-based trust computation, the trustworthiness level of an entity
is derived from how the entity behaves in the system as perceived by a subject
during its interaction with the entity. A subject identifies a positive
behaviour if the observed entity conforms to the prescribed protocols and
expectations, while negative behaviour corresponds to a deviation from
protocols and expected behaviour.

Data-based Trust Computation

In data-based trust computation, the trust values are calculated based on the
quality of data provided by an entity. For instance, in service-oriented CPS
applications, trust can be derived from the quality of the data acquired from
the data provider. Here, trust grows with the authenticity of the data, i.e.,
deliberate manipulation, noise or anomaly in the data would degrade the trust.
In this type of trust, data validation plays an integral role and one approach
to validate the data quality may include using correlated observations
obtained from other entities in proximity.

Hybrid Approach

Relying on a single type of trust may not be sufficient for deriving trust in
certain CPS applications, for instance, mobile crowd sourcing, wherein
trustworthy agents are seen as those who provide reliable data and conform to
the predetermined governance. In such scenarios, trust can be computed
considering both the data-based and behaviour-based characteristics. In the
hybrid approach, weightings are used to give favourable emphasis on either
data or behaviour characteristics.

####  2.1.3 Evidence Aggregation Approach

TRMS may adopt one of the evidence aggregation approaches to accumulate trust
evidence and calculate the final trust and reputation score. While there is an
exhaustive list of aggregation approaches [6], the following approaches are
among the most widely adopted:

Sum and Mean

The most intuitive and popular aggregation approach in TRMS is summation or
average of the aggregated trust evidence [7]. Due to its simple operation,
this method can also be validated manually to provide an objective
confirmation. Some weighting parameters may also be incorporated to give more
weight to recent or more important evidence. One of the challenges with this
approach is the determination of appropriate weights, which would have an
impact on the performance of the TRMS.

Flow Network

This approach is proposed in Advogato [8], wherein each participant is seen as
a node in the network, while the interactions between participants are
modelled as network flows. Consequently, the trust is derived from the number
of flows a participant obtained from others. This approach is relatively
robust to trust-related attacks, as the total active flows in the network are
assumed to be constant and strictly regulated by the TRMS.

Markov Chain

As implemented in EigenTrust [9], Markov chain approach works based on
probability modelling of a user’s feedback reaching at a particular
participant. The feedback from one user to another is modelled as a
probability function of a transition from source to target user, using which
the reputation score is derived from.

Bayesian

In this approach, the trust and reputation score are computed using
statistics. The trustworthiness score is described as a beta distribution of
two parameters where $\alpha$ and $\beta$ denote positive and negative
recommendation, respectively. To calculate and update the score, an update to
the provided beta distribution is performed, through which unfair ratings can
also be removed [10].

####  2.1.4 Trust Dimensions

In general, trust is strongly attached to a particular context and generally
cannot be transferred to another context without rigorous adjustment and re-
calculation. With this regard, context-awareness is an important factor to
consider in designing a TRMS. A TRMS can work on a single context or multiple-
context awareness in deriving trust from collected evidence depending on its
initial design [4].

Single-dimension

A lightweight TRMS with a simplified trust and reputation model might only
incorporate a single-dimension trust evaluation for the sake of limited
resources in CPS applications. While single-dimension trust model may not be
comprehensive, it may be preferred depending on the application design, e.g.,
when a majority of resource-constrained devices are in use.

Multi-dimension

On the other hand, multi-dimensional trust and reputation model represents
trust and reputation scores in multiple parameters or a single value derived
from multiple parameters with appropriate weightings. In practice, this type
of TRMS may require heavier computation and may not be suited for constrained
devices.

###  2.2 Adopting Blockchain for TRMS

Since its initial inception in 2009 as a pioneer in decentralised
cryptocurrency, blockchain has also been applied in many non-monetary
applications, one of which includes TRMS. While blockchain may introduce some
overheads, blockchain has promising potentials to be implemented in TRMS.
Here, we describe the inherent properties of blockchain that would bring
enhancements and benefits to TRMS.

Figure 2: Different types of architecture: centralised, distributed and
decentralised.

####  2.2.1 Decentralisation

Conventional TRMS relies on a third party aggregator to collect trust evidence
and calculate trust scores. Trusting a third party aggregator actually
introduces significant risks, for instance, when the aggregator is compromised
any underlying processes of trust computation could be maliciously altered and
the sensitive data could be in danger. On the other hand, blockchain removes
any Trusted Third Party (TTP) and comes with a decentralised architecture, as
seen in Figure 2, which eliminates associated risks of employing third party
aggregators in TRMS. Blockchain employs certain consensus algorithms, such as
Proof-of-Work and Proof-of-Stake, to enforce collaborative execution and
validation of transactions, using which fraudulent manipulations can be
avoided. The ledgers, in which the transactions or data are stored, are
replicated among all participants in the network that enhances availability.
In addition, blockchain can also be incorporated in a distributed TRMS to
enhance the mechanism, for instance, by utilising smart contracts, which we
describe in the following subsections.

####  2.2.2 Smart Contract

Smart contract is a form of execution codes agreed by a set of users, which
allows deterministic and trusted execution of business logic with reliable
guarantees that the process would be accomplished and validated
collaboratively in the network. Smart contracts can be embedded into a
blockchain-based TRMS to perform collection and calculation of trust scores
which can offload the trust computation from the CPS devices. For instance, a
node may submit feedback to the smart contract about the experience
interacting with a service provider, which later will be used to calculate the
service provider’s reputation score. Another node in the network can also
query the smart contract to obtain the reputation scores of particular service
providers. That is, a smart contract acts as a reliable intermediary for
computing and querying trust related information.

####  2.2.3 Pseudonyms

There is an inherent risk of leaking sensitive information in conventional
TRMS, as the authentication mechanism may link the identification details to
real-life identities. Blockchain introduces an elliptic curve public key
cryptography mechanism which utilises pseudonyms, i.e., public key, for
identification purposes, resulting in higher privacy preservation, as real
identities are not used. The use of pseudonyms is, to some extent, beneficial
for protecting users’ privacy, which is a desirable property in designing a
TRMS. In a blockchain-based TRMS, each node is identifiable by its public key
which hides any personal data, such as device ownership details. We discuss
more challenges and opportunities in privacy preservation for DTRMS in Section
4.2.

####  2.2.4 Immutable Storage

Traditional TRMS stores trust evidence and interaction history on each
device’s internal memory, which may overwhelm the devices, especially if there
is a large amount of information in a network with thousands of nodes. As
discussed earlier, a traditional TRMS can also rely on a TTP to keep track of
the trust related information, but with fundamental risk of data loss and
manipulation linked to the centralised approach. Blockchain data structure, as
depicted in Figure 3, enforces immutability as it is difficult if not almost
impossible to tamper the stored data on the blockchain. To tamper the data, an
attacker should break the hash cryptography and may need to traverse all the
way back to the genesis block. With proper removal of any Personally
Identifiable Information (PII), blockchain is a perfect and safe solution to
store interaction evidence that would later be used to calculate the trust
score.

Figure 3: An overview of blockchain’s immutable storage.

####  2.2.5 Transparency

Recall that in conventional centralised TRMS, any underlying mechanism in
trust and reputation calculation is performed by a centralised aggregator,
which conceals the actual process from other participants in the network. On
the other hand, blockchain offers transparent mechanisms in collaborative
trusted execution of business logic via smart contracts and transparent
immutable storage via a transparent shared ledger. With precautions in
handling and storing sensitive information in the ledger, this type of
transparent mechanism is preferred as it enables a traceable source of
evidence where any participant can ascertain the integrity of a trust
calculation by examining the ledger.

##  3 Use Cases

In this section, we outline various implementations of DTRMS for CPS across
different application domains to demonstrate how blockchain enhances and
brings benefits to these applications. We also present a summary of the use
cases in Table 1.

Table 1: Summary of DTRMS use cases.

Application Fields |  Goals |  Blockchain Features
---|---|---
Generic CPS |  end-to-end IoT trust establishment; securing CPS/IoT |  IS, AC, PS, SC
Supply Chain Managements |  managing traders and sellers; maintaining product quality |  SC, IS, PS
Crowd Sourcing |  selecting reliable workers |  AC, IS, PS
Robotic and Autonomous Systems |  selecting reliable service providers and detecting Byzantine nodes |  SC, IS, TR
Vehicular Ad-hoc Network |  validating exchanged messages to avoid malicious messages |  PS, AC, IS
IoT Data Marketplace |  curating traded data and providing fair payments |  SC, PS, IS
Distributed Energy Trading |  managing prosumers’ reliability |  SC, AC, IS

IS = Immutable Storage, AC = Adaptive Consensus, SC = Smart Contracts, PS =
Pseudonyms.

###  3.1 Generic CPS Trust Architecture

Blockchain ensures trusted, persistent and immutable storage for keeping
observational data which achieves tamper-proof storage and prevents unwanted
or malicious modification. However, the fundamental problem about establishing
trust in the data itself cannot be solved only by using blockchain. In [11],
the authors propose an end-to-end trust architecture for IoT. The authors also
proposed a dynamic block validation mechanism, wherein trust management
reduces the computation load on the nodes by reducing the amount of
transactions that need to be validated from trusted nodes.

In [12], the authors proposed a blockchain-based trust evaluation system for
Pervasive Social Networking (PSN), which helps to protect a node from
unfamiliar or unknown acquaintances in a trustless decentralised environment.
In the proposed solution, the trustworthiness of a participant is derived from
its social behaviour, represented as trust evidence stored on the blockchain.
The authors also proposed a trust-based consensus algorithm that aims to
reduce resource consumption and accelerate block generation in the consensus
process. A new block is confirmed if it is approved by an adequate number of
miners above a certain threshold.

In [13], a layered architecture, called BC-Trust, is proposed to provide a
scalable DTRMS solution for devices with high mobility in fog computing. In
BC-Trust, the trustworthiness of a particular service provider is derived from
a user’s direct experience and recommendations from known peers in the
network. Fog nodes with sufficient computing resources maintain the blockchain
and perform all required trust computation, offloading the trust computation
to high resource devices, which help to reduce the load in the constrained
devices. BC-Trust is also designed to be robust against known trust-related
attacks, such as ballot-stuffing and bad-mouthing (see Section 4.4).

A trust architecture can also be utilised to protect important CPS resources
from illegitimate access by unauthorised service consumers. In [14], the
authors proposed a decentralised Attribute-based Access Control (ABAC), in
which each service consumer is associated with a certain trust score based on
its behaviour in the network. The trust score of the service consumer is then
included in the required attributes to access resources. The authors
introduced three smart contracts, namely attribute provider, trust and
reputation system and policy smart contract to administer and operate the
trust-based access control. In [15], the authors extended their solution to
include privacy preservation and more efficient trust computation, where the
computation is offloaded to the blockchain entirely.

###  3.2 Supply Chain Management

In general, Supply Chain Management (SCM) systems demand for traceability,
which is fulfilled by the blockchain. In addition, blockchain allows multiple
writer to the system which is suitable in most instantiations of supply chain
wheres multiple stakeholders are involved. DTRMS is used in supply chains to
provide additional trustworthiness and benefits, as the quantified trust score
could be associated with certain product qualities or producers.

Bai et al. [16] proposed a trust management scheme in an e-agriculture supply
chain scenario where a network of smart greenhouses act as miners and form the
blockchain network. Each greenhouse manages a set of sensors that monitor the
condition of the farm. The farmers can use the network to query the sensors to
get some agriculture related information, such as the probability of whether
the farm needs to be fertilised or watered. The authors proposed a DTRMS in
which a game theoretic approach is used to determine the trustworthiness of
each sensor’s reading by associating a trust score to each sensor. When a
farmer queries for sensor data, the green house will also look for other
readings from related sensors and perform Bayesian inference to report an
estimate along with a trust score. A low trust score indicates that the
readings are incorrect and the sensor may need to be replaced.

Although blockchain can solve the immutability and traceability issues in
supply chain applications, the issue of the integrity of inserted data remains
unsolved. In [17], the authors proposed TrustChain, a three layered trust
management framework tailored for supply chains. The architecture consists of
three layers, namely data layer (for data input), blockchain layer (where all
process happens), and application layer (for transacting with blockchain). In
general, the reputation system assesses the quality of the commodities based
on multiple observations within the supply chain. The solution adopts smart
contracts to automate reputation calculation and specifically deploys two
contracts: 1) a quality contract to assess the quality of each supply chain
commodity based on sensor readings (e.g. temperature to keep the food quality)
and 2) a rating contract to compute the reputation of the traders. Each trader
has an inherent trust score, which is derived from all ratings with
customisable weightings. The method also utilises time-varying and amnesic
trust calculation where more emphasis is given to recent observations.

In [18], the authors proposed Reputation-based Trustworthy Blockchain Supply
Chain Management (RTB-SCM) to address trustworthiness issues in supply chains.
The solution is based on a consortium blockchain, named Reputation Assessment
Blockchain (RAB), that stores trade records and commodity information. RAB
introduces a token based reputation system, which is based on a cryptoasset
governed by the trusted regulator that runs the consortium blockchain. In
addition, the design utilises a smart contract based reputation rating model
that utilises tokens for rewards and punishments. The authors proposed two
algorithms; 1) Quality Status Generation (QSG) for quantifying $QInfo$, the
quality of a trade with regard to a commodity type derived automatically by
the sensors and 2) Token-based Reputation Reward/Punishment (TR2P) for
determining the appropriate reward or punishment based on $QInfo$ from QSG.

###  3.3 Crowd Sourcing

In [19], the authors proposed a hybrid blockchain architecture to enhance data
validation in crowd sourcing, wherein a private consortium blockchain is used
as the backbone of the network, while the public blockchain acts as a method
of transaction validation for the novel consensus protocol. In this work,
trust management is incorporated in a consensus algorithm called Proof-of-
Trust (PoT). The intuition is to select reliable validators to validate
collected data based on the trustworthiness score of the participants in the
crowd sourcing service. Combined with RAFT leader election and Shamir’s secret
sharing algorithm, the framework calculates the trust score based on three
independent parameters: 1) the number of transactions the user has on the
platform, 2) the total time the user has been involved in the validation
process and 3) the number of complaints the user receives. The PoT protocol
splits the consensus process into four phases, each of which is conducted by
different roles, which ensures performance and consistency of the consensus
process while greatly improving scalability.

In [20], Feng and Yan proposed MCS-Chain, which is a fully decentralised trust
management for Mobile Crowd Sourcing (MCS) purposes without relying on trusted
actors.. The architecture consists of end users, workers and miners. The
blockchain acts as the MCS platform, where all the procedures are recorded and
trust scores are evaluated. The miners in this case are the cell towers which
are responsible for managing the blockchain. A trust evaluation scheme is
designed to help the end users choose appropriate workers based on
reliability. End users post some particular tasks to the blockchain by
invoking blockchain transactions, which then broadcast the task to the workers
for bidding. In this step, the trust score helps the end users to pick the
preferred workers. The trust score of the workers are then updated based on
the feedback from the end users about certain tasks. To avoid unfair rating,
the mechanism applies deviation between personal and average feedback and also
considers the previous trust score of the submitter. The authors implemented
their solution on Android and Windows to evaluate the performance and
highlight the efficiency of the proposed system.

###  3.4 Robotic and Autonomous Systems

DTRMS has also been implemented in the area of robotics and autonomous
systems, where blockchain overcomes several reliability issues in information
sharing and aids the selection of service providers in a transparent way.

In [21], Alowayed et al. proposed a custom DTRMS which enables Autonomous
Systems to evaluate network providers based on their ability to provide
reliable interconnection service as per the pre-approved Service Level
Agreement (SLA). In this framework, the network performance measurements are
stored as transactions on a permissioned blockchain. A smart contract then
quantifies the trustworthiness of each network provider, called SLA score, and
analyses if the provider has submitted a misleading performance report. They
propose to use the SLA scores to select network providers to ensure that
clients are provided the required quality of service. The framework requires
each SLA score to be written on the blockchain with a transparent and
publicly-agreed SLA score calculation method between network participants,
while privacy preservation is achieved by adopting order-preserving encryption
mechanism [22].

Strobel and Dorigo proposed a blockchain-based knowledge sharing architecture
designed for swarm robotics [23]. In this framework, a DTRMS is employed to
identify Byzantine or malicious robots which may hinder the overall
performance of the system due to misleading data measurements. A permissioned
blockchain network is utilised, wherein each robot serves as an Ethereum node,
through which the robot could exchange knowledge to other robots within 50 cm
of proximity via blockchain transactions. The reputation for each robot is
calculated based on the absolute difference between reported observation and
the average of other observations from other robots in the proximity.

###  3.5 Vehicular Ad-Hoc Networks

Several works have been proposed in the field of Vehicular Ad-Hoc Network
(VANET) to incorporate DTRMS for enhancing the security or avoiding malicious
events in the network. The typical architecture of VANET includes mobile and
fixed nodes, e.g., the smart vehicles and Road Side Units (RSU), respectively.

The authors of [24] proposed a privacy preserving announcement protocol for
Internet of Vehicles (IoV) called PBTM. The authors also designed a
blockchain-based trust management system to ascertain the authenticity and
synchronise the timing of the exchanged messages. RSUs play an important role
of maintaining the blockchain and calculating the trustworthiness score of
each vehicle using weighted sums method according to the validity of the
transmitted message. Privacy preservation is achieved by adopting an identity-
based group signature, which realises the anonymity of each vehicle.

Wang et al. proposed BSIS: Blockchain-based Secure Incentive Scheme, a
reputation-based consensus protocol to obtain efficient consensus within a
Vehicular Energy Network (VEN) [25]. In the proposed consensus algorithm, the
validators are selected based on their trust score and each validator would
receive incentives upon successful execution of the consensus mechanism.
Consequently, the higher the trust score, the more chance a node has to be
selected as a validator and subsequently obtain the reward. The authors
classify two types of trustworthiness scores, namely local trust and
reputation value. The local trust score is derived from ratings obtained from
each interaction with other energy nodes, while the final reputation value is
calculated by aggregating all local trust scores that a node has obtained.

In [26], the authors proposed a blockchain based trust management for VANET,
wherein RSUs are the only approved actors to compute the trust values for each
vehicle. In this framework, each vehicle may send messages to other vehicles
from which the trust score of the vehicle is calculated. Each message receiver
generates ratings that represents the credibility of the corresponding
message. Due to high mobility and limited storage capacity, each vehicle is
expected to periodically submit the ratings to nearby RSUs, in which ratings
are aggregated and grouped to calculate the trust score of each vehicle using
Bayesian inference. Once the score is stored on the blockchain, any vehicle
may query the data if necessary. The authors argue that the proposed DTRMS
would give guidance to the vehicles about the quality of the received messages
and also provide the underlying evidence for reward and punishment mechanisms.

Lu et al. proposed a trust management system for VANETs, called BARS: a
Blockchain-based Anonymous Reputation System [27], with an emphasis on
anonymity by avoiding linkability between real and pseudo identities. In this
framework, the trustworthiness of each vehicle is determined from the
authenticity of the broadcast message and the reported opinion from other
vehicles. The authors introduced the concept of Law Enforcement Authority
(LEA) which is responsible for managing the framework and resolving disputes.

###  3.6 IoT Data Marketplace

The proliferation of CPS/IoT deployments has generated enormous amount of
data, using which the owner can obtain financial benefits by selling useful
data to specific consumers. In an IoT data marketplace, both sellers and
consumers can communicate and share data. However, the data customers do not
trust the sellers as the data quality cannot be guaranteed. Blockchain in this
case can enhance IoT data marketplace by providing a decentralised payment
mechanism using built-in cryptocurrency and utilising DTRMS to assess the
trustworthiness of each participant.

Camilo et al. proposed a blockchain-based data trading platform, in which
trust and reputation play an important role in helping customers determining
the quality of the sellers [28]. The authors define a distinction between
trust and reputation, where trust corresponds to buyer’s view of a seller
based on his trading experience, while reputation is an aggregated view of a
particular seller from multiple individual trust scores across different
buyers. In this platform, the data owners or sellers may advertise the
metadata via a smart contract on the blockchain, which can be be explored by
data buyers to select the preferred sellers. The data buyers are required to
submit a feedback that rates the seller and the data quality through a
feedback transaction to the blockchain, using which the smart contract
evaluates the trust and reputation score of the sellers accordingly.

In [29], the authors proposed an automatic review system to assess the quality
of the data, which is used for monetisation of IoT data. The system adopts a
publish-subscribe mechanism, where a MQTT broker plays an important role along
with the permissioned blockchain. A rating is associated to each data on each
topic in the platform. Any data buyer may request the smart contract to browse
available data based on subscribed topics, with reviews associated with each
data sale. Upon completing the data access, each buyer is required to submit
reviews about the accessed data for an incentive. The system utilises a smart
contract to achieve an automated payment and incentive mechanisms that
eliminate the need for a TTP, while also reducing the associated risks of
trusting an external party.

The authors in [30] proposed a reputation system for online market places,
which is based on hashcash proof-of-work algorithm, originally designed to
reduce spams in email [31]. The reputation system is designed to assess and
incentivise watchtowers to behave rationally. A watchtower is an independent
entity that preserves the client’s interest for specific purposes, e.g.,
lightning payment network [32], by continuously monitoring the blockchain
network on behalf of the clients that may frequently be offline. The
watchtowers offer a monitoring service in the open market, where clients would
tend to pick the watchtowers with the highest reputation score. During the
negotiation phase with a client, a watchtower publishes a smart contract as a
persistent proof that bonds the terms and conditions of payments and services
with the client. The reputation of the watchtowers is derived from successful
transactions, while a proof-of-breach is generated from the contract if a
watchtower does not fulfil its obligation.

In [33], the authors proposed a payment mechanism for IoT marketplace, called
Secure Pub-Sub (SPS), where blockchain is utilised to provide fair payments
and reliability. In SPS, blockchain performs as a payment gateway between a
publisher and subscriber that do not necessarily have to trust each other,
wherein a subscriber can deposit some funds prior to subscribing to a
particular publisher to access the data. In addition, a reputation system is
employed so that each subscriber can assess the publisher after accessing its
service, where a smart contract transparently maintains the process. Here, the
reputation system can help the subscribers to pick appropriate publishers
based on certain reputation scores that are higher than a threshold for
determining reliable publishers.

###  3.7 Distributed Energy Trading

Distributed or peer-to-peer energy trading is a marketplace where each
prosumer, a user who consumes and produces energy, can transact energy to the
end user directly without the need of a central entity. It has been
demonstrated by recent works that TRMS can help improve the efficiency and
enhance the fairness of energy trading.

In [34], the authors proposed a secure blockchain-based energy trading
platform with a built-in reputation system to enhance the reliability and
encourage honest behaviour among blockchain nodes. The authors also
incorporate reputation scores into the Proof-of-Work consensus algorithm,
called PoWR, to reduce the block creation time and overall latency. Each
participant utilises the blockchain network as a communication channel,
through which each participant exchanges information about direct and indirect
trust experiences. In this reputation framework, a higher reputation score
corresponds to higher probability of participating in the PoWR, hence more
chance of getting incentives.

Khorasany et al. proposed a peer-to-peer framework for energy trading in [35],
where blockchain and smart contracts are employed to build a decentralised
trading mechanism. The authors also associate a reputation factor to each
energy agent that represents its reliability in fulfilling the obligations. In
addition, the authors also introduced an algorithm, called Anonymous Proof of
Location (A-PoL), to anonymously prove user’s location. In performing the
energy trading, each user may select a partner based on their preference on
both reputation factor and agent’s location, which is handled by an automated
algorithm. A Dispute Resolution smart contract is in charge of calculating
each agent’s reputation factor which is based on prior commitments in
delivering energy to the trading counterpart.

A framework called Reputation for Blockchain-based energy Trading (RBT) is
proposed in [36]. RBT utilises blockchain as a traceable and immutable storage
for reputation scores and smart contract for automated reputation calculation.
Here, the reputation is derived from the behaviour of each node according to
its role in the P2P process via three parameters, namely role, rule and
reputation. A matchmaking strategy based on $k$-double auction algorithm is
used to connect both buyers and sellers and to decide trading prices that are
more beneficial to both parties. The matchmaking strategy also includes a
fairness indicator, which is a ratio between reputation score and the average
income and cost for sellers and buyers, respectively.

##  4 Challenges and Future Directions

There are many open research directions for DTRMS. In this section, we discuss
several issues and challenges that need to be addressed for future research on
DTRMS, which include scalability, privacy, excessive resource consumption,
security and interoperability.

###  4.1 Scalability

Blockchain requires a transparent shared ledger which is replicated between
blockchain participants to enforce redundancy and maintain consistency. While
the replication removes a single point of failure and increases availability,
the shared ledger may grow significantly due to the append-only nature of
blockchain storage mechanism. In the long run, the explosion of storage
requirement may hinder the performance of the network as it demands high
memory requirements, which causes high synchronisation times for new node
initialisation. For instance, as of June 2021, Ethereum blockchain size has
reached approximately 820GB for a default full
node111https://etherscan.io/chartsync/chaindefault and 7.5 TB for a full
archival node222https://etherscan.io/chartsync/chainarchive and is expected to
grow by approximately 75GB per year.

In addition, blockchain is known to have a limitation in block generation
time, which contributes to the scalability issues. While Visa payment network
could achieve up to 47,000 transactions per second (tps), Bitcoin is only able
to cater approximately 7 tps with limited block size of 1 megabyte [32]. The
fixed rate of block generation time introduces a bottleneck, and could be
exacerbated if there are more transactions to be processed.

DTRMS is thus faced with potential scalability issues inherent to the
underlying blockchain. The growing number of participants in the network would
also deteriorate the scalability of DTRMS, as accommodating a large number of
nodes results in large storage requirements. It is known that addressing
scalability for blockchain is still an open research problem and the community
is still actively proposing new solutions. Some methods to overcome
scalability issues in a DTRMS may include:

Figure 4: The intuition of sharding for improving scalability.

Sharding

In this mechanism, a full copy of the shared ledger that represents the
current state of the blockchain is separated into several chunks which are
distributed to different nodes. That is, each node would retain different
parts of the whole ledger which significantly reduces the storage requirement
to store the entire blockchain state (see Figure 4).

Scalable Reputation Scheme

The reputation model can be adjusted to circumvent the scalability issue by
storing a collective or aggregated trust information that resembles a group of
devices instead of storing trust information for all devices, which may be
redundant [5]. These storage mechanisms may help reducing the amount of data
stored in the ledger, which helps to alleviate the scalability issue. For
example, the reputation model may average the trust score of a group of
devices from the same owner prior to storing the record in the ledger.

Off-chain computations

As demonstrated in the Lightning network of Bitcoin [32], off-chain
computations would result in significant reduction of processing latency, as
the transactions are processed off-chain without going through excessive
consensus processes. In this scenario, two parties retain a signed contract
that resembles the current token balance of each party and continue the
transactions off-chain by keeping a signed log of balance transfers. An
overlay network can also be constructed from multiple-signed contract of
different owners, i.e., the so called Lightning network. The contract is
ceased when a party submits a final transaction to the main ledger that
transfers the tokens according to what is recorded in the signed logs.

###  4.2 Privacy

One of the main motivations of incorporating blockchain for TRMS is the
inherent use of pseudonyms instead of real identities for authentication
purposes. Using public keys as pseudonyms for authentication would conceal the
actual identities of the users and make it difficult to link the public keys
to the real user identities, a preferred requirement for privacy preservation.
However, research has shown that it is possible to track user behaviour from
pseudonym-based transaction logs and link them back to the real identities
[37]. Ideally, a DTRMS should achieve privacy preservation by addressing the
following concerns [7]:

User anonymity

The goal of user anonymity is to conceal the actual identity and prevent
linkage attacks. For example, a user can be represented by more than one
pseudonym, e.g., replaceable public keys, that would allow the users to
continue transacting in the system without allowing a malicious entity to link
the multiple pseudonyms to their actual identities.

Feedback confidentiality

In practice, total user anonymity is difficult to achieve and some information
regarding the identities or interactions of users might be inferred from the
data revealed by the applications. However, it is a critical requirement to
ensure the confidentiality of feedback information submitted by the users in
order to encourage users to submit truthful feedback.

The research community in the field of security and privacy still actively
proposes new methods for privacy preservation, some of which can be
implemented for DTRMS:

Zero-Knowledge Proofs

Using Zero-Knowledge Proofs (ZKP), one can verify the validity of a statement
by getting a plausible proof without exposing any additional information other
than the validity of a statement, hence preserving the privacy [38]. ZKP can
be implemented to provide a means to validate a particular pseudonym without
any risk of revealing sensitive information that might be linked back to the
real identity of a user.

Homomorphic Encryption

Typically, a generic encryption algorithm provides a means to conceal
sensitive information to preserve privacy. However, the encrypted data, i.e.,
ciphertext, is practically unusable and decryption is required for the
ciphertext to be usable, which may expose sensitive information to public.
Homomorphic encryption, on the other hand, allows computations on encrypted
data without the need of decryption, which may help to keep the sensitive
information hidden. For instance, in DTRMS homomorphic encryption permits
several operations on encrypted feedback to achieve feedback confidentiality.

Secure Multi-Party Computation

Hiding sensitive information can be achieved by utilising Secure Multi-Party
Computation (SMPC), using which several input values can be aggregated into an
output value without revealing the individual input values. SMPC can be
implemented in a DTRMS to calculate trust or reputation scores by aggregating
several feedback values, while keeping the feedback values private.

###  4.3 Resource Consumption

The traditional centralised TRMS approach utilises a single TTP that acts as a
single authoritative entity for managing resources and making decisions. While
there is an inherent risk in trusting a TTP, this centralised architecture
results in a relatively low resource consumption. On the other hand, the
absence of TTPs in DTRMS requires a distributed consensus algorithm that
typically consumes high resources while also sacrificing latency and
throughput. For instance, Bitcoin is notorious for its low block generation
rate of 10 minutes and high carbon footprint from the coin mining process
[39].

In general, CPS applications may consist of thousands of interconnected
constrained devices that demand high throughput and low latency. One possible
solution to overcome the high resource consumption of blockchain is to devise
a tailored consensus algorithm for CPS that suits the constraints of typical
CPS devices [40]. In [41], the authors proposed a blockchain platform tailored
for CPS/IoT architecture, called Lightweight Scalable Blockchain which (LSB),
where the computation and storage capacity are typically constrained. LSB
employs a lightweight consensus algorithm and distributed throughput
management specifically designed for CPS/IoT.

As discussed in previous sections, a reputation system can be implemented to
create an efficient consensus algorithm with less overheads. In reputation-
based consensus algorithm, a Proof-of-Work consensus algorithm that demands
solving a mathematical puzzle for verifying a block is replaced with a Proof-
of-Stake like consensus where the block validator is selected based on its
reputation in the network [42]. While reputation-based approach is still prone
to trust or reputation attacks (see Section 4.4), it can reduce the
computational load for achieving a distributed consensus to determine the next
block to be mined.

###  4.4 Security

As both traditional and decentralised TRMS rely on multiple untrusted parties
to gain collective knowledge for building reputation scores, any entity in the
system can launch attacks that may impact the normal operation of the system.
There are known attacks in both traditional and decentralised TRMS, which are
commonly referred to as trust-related attacks. A DTRMS should be resilient to
these attacks, as noted in the following:

Sybil Attack

In this type of attack, an adversary creates several forged identities that
can be utilised to gain disproportionate influence against a benign user with
a single truthful identity. The adversary may illegitimately utilise the
forged identities to launch attacks such as ballot-stuffing or bad-mouthing
for its own benefits. Sybil attacks can be avoided by increasing the cost of
creating new identities [43]. Also, when privacy is not a key requirement,
linking to real identity is proven to be effective in preventing Sybil attack
[44].

Ballot-stuffing

An adversary can illegitimately increase its reputation score by launching a
Sybil attack to submit multiple fake feedback or by colluding with other
adversaries. This attack is called ballot-stuffing and is also often referred
to as self-promotion attack. The risk of ballot-stuffing attack can be partly
reduced by using coins to submit feedback [4], which would impose a
significant cost to the adversaries for submitting multiple feedback by
themselves, while the advantage from the attack may not be worthwhile.

Bad-mouthing

In contrast to ballot-stuffing, bad-mouthing is an attack that aims to ruin
another honest user’s reputation score by providing negative feedback
regardless of the behaviour of the target user. Bad-mouthing attacks could
lead to severe damaging effects, especially for sensitive applications, such
as monetary systems [7]. While mitigating this effect is a non-trivial task,
one possible protection could be to compare feedback from unknown entities to
those from highly trusted nodes [45].

Whitewashing

When the adversaries have low or negative reputation scores, they can rejoin
the system with a new identity resulting in a fresh reputation score. This
type of attack is attractive to the adversaries especially when the cost of
re-entering the system is very minimal. As a mitigation scheme, the system may
require users to link the identity or pseudonym with a real-world identity,
e.g., a website, that would incur significant cost for modification [46].

On-off Attack

The adversary may act opportunistically by providing alternating feedback,
i.e., positive and negative, to maintain the reputation score at a safe level
to avoid detection. For instance, the adversary may constantly provide
positive service to get selected as a service provider, but once selected, the
adversary launches the attack by providing poor services to the selected
target nodes [47]. Appropriate weighting according to temporal dynamics in the
reputation formula would help to reduce the risk of this attack [7]. In this
approach, higher weights are applied to recent or more important interaction
evidence, resulting in significant decline of the trust score in case of an
attack.

###  4.5 Interoperability

In practice, it is common for CPS applications to have a wide range of
technological implementations with different types of hardware and protocols
for communications. Consequently, these implementations tend to work in
isolation with very minimal cross-platform collaboration. Interoperability is
an important factor to consider in designing CPS solutions for achieving
efficient collaboration across platforms and applications. For instance, in a
blockchain-based smart city architecture, interoperability would allow
transfer of digital assets and values across different blockchain platforms
[48]. There are several initiatives in blockchain interoperability, which
include Cosmos333https://cosmos.network/ and
Interledger444https://interledger.org/.

In addition, carefully modelled interoperability schemes would also allow
transfer of trust and reputation scores across different platforms. In any
TRMS application, the lack of prior interactions or recommendations is a
challenge in quantifying reputation, which could be mitigated by transferring
reputation values from a separate platform that has already gathered some
evidence about the trustworthiness of a particular entity. Note that context
awareness should also be taken into account in this mechanism, as trust is
highly associated with the underlying context and may not be transferable from
one context to the other.

In the context of DTRMS for CPS, interoperability would also mean an ability
to appropriately derive trust and reputation scores across heterogeneous
constrained devices. As typical CPS applications include a variety of hardware
types with different capabilities in power, computation and storage, the trust
and reputation model should take into account these conditions of
heterogeneity [5]. For instance, when the trust score is derived from the
computational power of a device, the model should apply an appropriate
weighting for high-performance and constrained devices to overcome the
heterogeneity in computational power.

##  5 Conclusion

The goal of a Trust and Reputation Management Systems (TRMS) is to assess and
quantify the trustworthiness of each participant in the system for
safeguarding users from the risk of interacting with untrustworthy entities.
Adoption of blockchain to TRMS can benefit and enhance TRMS. In this article,
we presented how the salient features of blockchain can enhance TRMS. We
specifically focused on four features of blockchain, namely decentralisation,
smart contracts, pseudonyms, immutable storage and transparent mechanism. We
described several implementations of DTRMS across different fields of
applications. We also discussed several open challenges and future directions
for research in the area of DTRMS.

## Acknowledgement

This work was supported by the Cyber Security Research Centre Limited through
the Australian Government’s Cooperative Research Centres Programme.

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