One more re-identification demonstration, and then I’m out

What should we do about re-identification? Back when I started this blog in grad school seven years ago, I subtitled it “The end of anonymous data and what to do about it,” anticipating that I’d work on re-identification demonstrations as well as technical and policy solutions. As it turns out, I’ve looked at the former much more often than the latter. That said, my recent paper A Precautionary Approach to Big Data Privacy with Joanna Huey and Ed Felten tackles the “what to do about it” question head-on. We present a comprehensive set of recommendations for policy makers and practitioners.

One more re-identification demonstration, and then I’m out. Overall, I’ve moved on in terms of my research interests to other topics like web privacy and cryptocurrencies. That said, there’s one fairly significant re-identification demonstration I hope to do some time this year. This is something I started in grad school, obtained encouraging preliminary results on, and then put on the back burner. Stay tuned.

Machine learning and re-identification. I’ve argued that the algorithms used in re-identification turn up everywhere in computer science. I’m still interested in these algorithms from this broader perspective. My recent collaboration on de-anonymizing programmers using coding style is a good example. It uses more sophisticated machine learning than most of my earlier work on re-identification, and the potential impact is more in forensics than in privacy.

Privacy and ethical issues in big data. There’s a new set of thorny challenges in big data — privacy-violating inferences, fairness of machine learning, and ethics in general. I’m collaborating with technology ethics scholar Solon Barocas on these topics. Here’s an abstract we wrote recently, just to give you a flavor of what we’re doing:

How to do machine learning ethically

Every now and then, a story about inference goes viral. You may remember the one about Target advertising to customers who were determined to be pregnant based on their shopping patterns. The public reacts by showing deep discomfort about the power of inference and says it’s a violation of privacy. On the other hand, the company in question protests that there was no wrongdoing — after all, they had only collected innocuous information on customers’ purchases and hadn’t revealed that data to anyone else.

This common pattern reveals a deep disconnect between what people seem to care about when they cry privacy foul and the way the protection of privacy is currently operationalized. The idea that companies shouldn’t make inferences based on data they’ve legally and ethically collected might be disturbing and confusing to a data scientist.

And yet, we argue that doing machine learning ethically means accepting and adhering to boundaries on what’s OK to infer or predict about people, as well as how learning algorithms should be designed. We outline several categories of inference that run afoul of privacy norms. Finally, we explain why ethical considerations sometimes need to be built in at the algorithmic level, rather than being left to whoever is deploying the system. While we identify a number of technical challenges that we don’t quite know how to solve yet, we also provide some guidance that will help practitioners avoid these hazards.

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