Bonus Content: Privacy’s Meaningful Purpose

A few years ago, I dreamed up a concept of “meaningful privacy” to better define the discussion around the broad topic of privacy. I noticed that not every piece of data is equal. Some things are kept private because there is a concern of actual harm if the information is publicized. Some other things are kept private because of societal or cultural norms and traditions. Privacy is not and end in itself- we have it for the purpose of protecting information. However, different data has different value. Therefore, the value of privacy is relative, varying according to the data in question. One effect of this concept is to treat different breaches according to the type (or value) of data in question.

There is a huge and illuminating problem with this idea of “meaningful privacy”: Just because someone didn’t steal anything from your house doesn’t mean you feel comfortable about a break-in. Although privacy is not an end in itself, it is intrinsically upsetting when our privacy is violated. The biggest fear is the potential for future violations of privacy: just because no harm occurred as a result of one violation, there is no guarantee about future violations. Furthermore, a past violation of privacy indicates a vulnerability and thus the potential for future violations. With a diminished expectation of privacy, there is diminished privacy. Privacy is of little use if it cannot be relied upon.


Horizon: The Dawn of Zero Privacy?








II. A Focus on Connected Devices








Privacy (as the Withholding of Information) in the Information Age

Business professionals in e-commerce talk about information like it is today’s fundamental commodity. Yet information— raw data— is less helpful than we tend to think. Privacy becomes harder to maintain in an era in which business and government think that more data is always better and that accruing data will solve problems. Information is necessary, but not sufficient, to solving problems and pushing progress along.

Lots of entities want information: governments want information about their citizens, employers want information about their employees, corporations want information about their consumers, etc. Such entities have always wanted information, but only recent technological developments have made it reasonable to obtain and organize that information. The biggest remaining barrier to such information collection is the ethical and legal concept of privacy. My contention is that the mere gathering of data is less helpful than the gatherers might think.

One way to think of this issue is to see human action as having two components: 1) an internal motivation or attitude and 2) an external display of action. So, if I purchase a large supply of plastic drinking cups, the store computers may recognize my purchase and correlate it to the kinds of other items people purchase with drinking cups: plastic cutlery, snack food, soda, and so forth. The store wants to predict my motivation by examining my action and correlating my action with similar actions and using inductive reasoning to sell me more things. But what if my motivation in buying many cups is to have a cup stacking competition? Or to have a 2nd grade class plant lima beans? The problem with relying heavily on gathering information is that you can only make guesses about the internal state of the actor.

The debatable assertion is this: Humans cannot be captured by data sets. Some (who probably favor Hume) may say they can, but it must be conceded that the data set must become extremely, extremely large. Perhaps more importantly, some elements essential to that data set cannot be collected through transaction records, e-mails, Facebook “likes”, tweets, and all other collectable data. Seen in this way, a reasonable fear emerges: as entities gather data, they act on that data as though it is a more complete picture than it actually is. Another way to state this issue is “data does not explain itself.”

There are a few important takeaways about the limits of the power of data:

1) You don’t get to know people from their Facebook profiles.

2) Stores know what people buy, but not always why they buy them.

3) Privacy can protect both parties from an incomplete picture.

4) Data is a raw material. It must be processed with understanding, refined through meaning and context, and crafted with wisdom into usable information and then into intelligence.

5) Computer systems can record observations of fact and interact according to algorithm, but cannot “understand” any “significance” or “meaning” of any data.

NOTE: There is so much to this subject! I expect to return (probably repeatedly) to this subject in more specific settings to explore deeper nuances and applications of issues.