I've been reading up recently on differential privacy and I'm just starting to understand it. I've also read this paper that basically determined the sexual orientation of a user using Facebook friendships.

My question is, how can differentially privacy be applied in this certain scenario?


closed as unclear what you're asking by Michael Chernick, Peter Flom Apr 4 at 11:00



Differential privacy is an example of a privacy enhancing technology that acts on data during a computation (such as during training on a machine learning model, or via a query mechanism over an aggregated set of data). In this "global" privacy setting, the raw data is stored somewhere but derived data (e.g. the machine learning model or the output statistic of a query mechanism) is considered differentially private if it does not reveal the presence or absense any any single user's contribuiton to the original dataset.

The paper that you link to uses publicly available Facebook data (demographic data and connections between users). In order for differential privacy to be "applied" to this scenario, Facebook would have to build and maintain a query service and restrict access to raw data such that only noisy aggregate statistics are returned. For example, you might query how many people with certain attributes identify as LGBTQ+ and you would get a single number with some added random noise to protect the privacy of the individual users. Alternatively, Facebook could reveal individual user statistics using a "local" differential privacy setting. In this case you would visit a single user's profile and the information returned would be very noisy. For example a 30 year old person's profile might show his age as 35 or 27 with noise added.

Obviously, in reality, Facebook would never employ either of these two applications of differential privacy. It is not in Facebook's interest to hide this information from users and so anyone who associates themselves with Facebook risks breaches of privacy such as those discussed in the paper.

  • $\begingroup$ Thank you for your answer! But I have more questions: 1. So is it improbable that online social networks will employ differential privacy, whether global or local? 2. I've read that Google has been using local differential privacy to get analytics for Chrome. Same with Apple for keyboard usage and emoji statistics. But I don't know of any query service that employs global differential privacy. Does this mean global DP is impractical to use in the industry or in practice? $\endgroup$ – L. Lei Apr 3 at 2:45
  • $\begingroup$ I don't think it's improbable per se. It depends what information the social network wants to disclose. For example, there may come a time in the future where Facebook decides it wants to differentiate itself from other services by improving privacy on its platform. One way they could do this is with ad targetting. Facebook could employ global differential privacy to show advertisers stats about the audience they might expect to reach. Or, they could add noise to the output of general API queries for particularly sensitive attributes to satsify differential privacy. $\endgroup$ – Chris Briggs Apr 4 at 10:49
  • $\begingroup$ Other real world examples of global differential privacy in action are: US Census disclosure: https://digitalcommons.ilr.cornell.edu/ldi/49/ + a few here too: https://security.stackexchange.com/questions/66531/real-world-applications-of-differential-privacy $\endgroup$ – Chris Briggs Apr 4 at 10:54
