More on Las Vegas

Las Vegas is an interesting example for those who think about adversarial knowledge discovery because it shows how little people really value privacy. The casinos leave their customers no privacy — their every action is captured while they’re in the casino, and even making a hotel reservation starts a chain of analysis in motion. Imagine the fuss if a government did anything remotely like this!

I mentioned Jeff Jonas yesterday. He has made two contributions to adversarial knowledge discovery:

  1. An agnostic approach to people’s identity in data. One of the problems of data analysis, particularly when the data comes from multiple sources, is putting together the attributes that belong to the same person. Usually some kind of key is used, perhaps a biometric, so that records that belong together can be discovered to belong together. If you are trying to hide some of your data, you want to confuse the key as much as possible.There are many ways to do this, but the best ones are ones that can be disavowed if the question ever comes up. So people who are trying to hide use variants of their names, mix up digits of phone numbers and street addresses, and anything else that can also happen accidentally. Some studies of criminal records have shown that nearly half have been altered in this kind of way.Jonas’s Non-Obvious Relationship system allows records that might belong to the same person to coexist. When some analysis is done of the connections between people, which is where this kind of blurring matters because it increases the distances between the nodes representing people, the software can make an on-the-fly determination of how confident it is that a set of records represent the same person, without making the determination irreversible.
  2. He argues that any data analysis system should treat the queries that are made to it as new parts of the data, that should be added to the system and kept in it. This has two advantages. The first is that, if the answer to a query appears in the data after the query has been made, the answer can still be provided to the person who asked. The second is that a second query of the same kind can produce a response about the existence of the first query. In other words, one person can discover that someone else was asking the same question. Both of these are important and useful in adversarial settings, and should be considered for other data-analysis systems.

Jonas’s blog is
here.

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