Reidentification as Basic Science
What really drives reidentification researchers? Do we publish these demonstrations to alert individuals to privacy risks? To shame companies? For personal glory? If our goal is to improve privacy, are we doing it in the best way possible?
In this post I’d like to discuss my own motivations as a reidentification researcher, without speaking for anyone else. Certainly I care about improving privacy outcomes, in the sense of making sure that companies, governments and others don’t get away with mathematically unsound promises about the privacy of consumers’ data. But there is a quite different goal I care about at least as much: reidentification algorithms. These algorithms are my primary object of study, and so I see reidentification research partly as basic science.
Let me elaborate on why reidentification algorithms are interesting and important. First, they yield fundamental insights about people — our interests, preferences, behavior, and connections — as reflected in the datasets collected about us. Second, as is the case with most basic science, these algorithms turn out to have a variety of applications other than reidentification, both for good and bad. Let us consider some of these.
First and foremost, reidentification algorithms are directly applicable in digital forensics and intelligence. Analyzing the structure of a terrorist network (say, based on surveillance of movement patterns and meetings) to assign identities to nodes is technically very similar to social network deanonymization. A reidentification researcher that I know who is a U.S. citizen tells me he has been contacted more than once by intelligence agencies to apply his expertise to their data.
Homer et al’s work on identifying individuals in DNA mixtures is another great example of how forensics algorithms are inextricably linked to privacy-infringing applications. In addition to DNA and network structure, writing style and location trails are other attributes that have been utilized both in reidentification and forensics.
It is not a coincidence that the reidentification literature often uses the word “fingerprint” — this body of work has generalized the notion of a fingerprint beyond physical attributes to a variety of other characteristics. Just like physical fingerprints, there are good uses and bad, but regardless, finding generalized fingerprints is a contribution to human knowledge. A fundamental question is how much information (i.e., uniqueness) there is in each of these types of attributes or characteristics. Reidentification research is gradually helping answer this question, but much remains unknown.
It is not only people that are fingerprintable — so are various physical devices. A wonderful set of (unrelated) research papers has shown that many types of devices, objects, and software systems, even supposedly identical ones, are have unique fingerprints: blank paper, digital cameras, RFID tags, scanners and printers, and web browsers, among others. The techniques are similar to reidentification algorithms, and once again straddle security-enhancing and privacy-infringing applications.
Even more generally, reidentification algorithms are classification algorithms for the case when the number of classes is very large. Classification algorithms categorize observed data into one of several classes, i.e., categories. They are at the core of machine learning, but typical machine-learning applications rarely need to consider more than several hundred classes. Thus, reidentification science is helping develop our knowledge of how best to extend classification algorithms as the number of classes increases.
Moving on, research on reidentification and other types of “leakage” of information reveals a problem with the way data-mining contests are run. Most commonly, some elements of a dataset are withheld, and contest participants are required to predict these unknown values. Reidentification allows contestants to bypass the prediction process altogether by simply “looking up” the true values in the original data! For an example and more elaborate explanation, see this post on how my collaborators and I won the Kaggle social network challenge. Demonstrations of information leakage have spurred research on how to design contests without such flaws.
If reidentification can cause leakage and make things messy, it can also clean things up. In a general form, reidentification is about connecting common entities across two different databases. Quite often in real-world datasets there is no unique identifier, or it is missing or erroneous. Just about every programmer who does interesting things with data has dealt with this problem at some point. In the research world, William Winkler of the U.S. Census Bureau has authored a survey of “record linkage”, covering well over a hundred papers. I’m not saying that the high-powered machinery of reidentification is necessary here, but the principles are certainly useful.
In my brief life as an entrepreneur, I utilized just such an algorithm for the back-end of the web application that my co-founders and I built. The task in question was to link a (musical) artist profile from last.fm to the corresponding Wikipedia article based on discography information (linking by name alone fails in any number of interesting ways.) On another occasion, for the theory of computing blog aggregator that I run, I wrote code to link authors of papers uploaded to arXiv to their DBLP profiles based on the list of coauthors.
There is more, but I’ll stop here. The point is that these algorithms are everywhere.
If the algorithms are the key, why perform demonstrations of privacy failures? To put it simply, algorithms can’t be studied in a vacuum; we need concrete cases to test how well they work. But it’s more complicated than that. First, as I mentioned earlier, keeping the privacy conversation intellectually honest is one of my motivations, and these demonstrations help. Second, in the majority of cases, my collaborators and I have chosen to examine pairs of datasets that were already public, and so our work did not uncover the identities of previously anonymous subjects, but merely helped to establish that this could happen in other instances of “anonymized” data sharing.
Third, and I consider this quite unfortunate, reidentification results are taken much more seriously if researchers do uncover identities, which naturally gives us an incentive to do so. I’ve seen this in my own work — the Netflix paper is the most straightforward and arguably the least scientifically interesting reidentification result that I’ve co-authored, and yet it received by far the most attention, all because it was carried out on an actual dataset published by a company rather than demonstrated hypothetically.
My primary focus on the fundamental research aspect of reidentification guides my work in an important way. There are many, many potential targets for reidentification — despite all the research, data holders often (rationally) act like nothing has changed and continue to make data releases with “PII” removed. So which dataset should I pick to work on?
Focusing on the algorithms makes it a lot easier. One of my criteria for picking a reidentification question to work on is that it must lead to a new algorithm. I’m not at all saying that all reidentification researchers should do this, but for me it’s a good way to maximize the impact I can hope for from my research, while minimizing controversies about the privacy of the subjects in the datasets I study.
I hope this post has given you some insight into my goals, motivations, and research outputs, and an appreciation of the fact that there is more to reidentification algorithms than their application to breaching privacy. It will be useful to keep this fact in the back of our minds as we continue the conversation on the ethics of reidentification.
Thanks to Vitaly Shmatikov for reviewing a draft.