More than a year ago I wrote an article with the provocative title: Is Differential Privacy practical? The post was essentially one big buildup for an epic follow-up post that I simply never wrote. Since then dozens have asked me for an answer to this urgent question. Recently, after the post hit the front page of Hacker News, half a dozen emails inquired about the follow-up post that I had promised. Some speculated that owing to Betteridge’s law of headlines, the answer was simply no.
Despite my venerable history of failing on various commitments and my apparent peace with it, this situation went too far even by my own low standards. So, I decided to write a not so epic version of that promised blog post.
The California Public Utilities Commission
I’ll arrange my thoughts around the case of the California Public Utilities Commission (CPUC). The CPUC is a regulatory agency that regulates privately owned public utilities in California. In recent years there has been political pressure on the utilities to give third parties access to smart meter data. As discussed in my previous post, smart meter data is of enormous value to many, but comes with serious privacy challenges.
To settle these issues the CPUC organized a major legal proceeding with the goal of creating rules that provide access to energy usage data to local government entities, researchers, and state and federal agencies while establishing procedures that protect the privacy of consumer data.
I served as a privacy expert within the proceeding together with Cynthia Dwork, Lee Tien from the EFF, and Jennifer Urban and her team from Berkeley. Our goal was to inform various parties about the pitfalls of insufficient privacy mechanisms and to propose better ones. Our proposed solution focused on differential privacy for the uses cases in which it made sense. There were a number of use cases that the CPUC considered. Not all of them were well suited for differential privacy to begin with.
A proposed decision
My involvement with the case ended in 2014 after a proposed decision of the administrative judge. To summarize a 120 page document in one sentence, the ruling did not endorse differential privacy strongly enough for me to further pursue the case actively. Nevertheless, there was still significant interest in differential privacy from some of the utilities. I believe that one utilities company engaged with Microsoft with the goal of building a prototype of a differentially private solution for their data sharing needs.
The ruling was disappointing from my perspective in that it did not advocate the use of differential privacy in any of the use cases. Meanwhile it shot down several uses cases essentially not giving the use case sponsors meaningful access to energy data at all. In those cases differential privacy could’ve provided an obviously better trade-off for everyone.
The ruling didn’t so much reflect a technical verdict about differential privacy. Rather it reflected our inability to successfully anticipate and maneuver the highly complex political and legal environment in which the decision was made.
A post mortem
Our proposal based on differential privacy initially met with resoundingly positive responses when we first presented it to the administrative judge and various parties present in the meeting. We did however face bitter opposition from a group of researchers who sponsored one use case. Those researchers, who had been working with raw smart meter data in the past, were worried that differential privacy would create an obstacle for them. We quickly realized that it would be difficult to agree with them on the extent to which their research practices are compatible with differential privacy. So, we specifically excluded their use case from the scope of our proposal focusing on some of the remaining use cases instead. This didn’t stop the researchers from lobbying relentlessly against differential privacy. In particular, they filed a last minute comment in which they attacked differential privacy sharply based on many profound factual misunderstandings of the privacy notion. Due to the perfect timing of their comment, we were unable to submit a rebuttal. In the end, I believe this alone was enough for the administrative judge to conclude that the use of differential privacy was at present too controversial to be proposed as a solution in the ruling.
My point is not to criticize this group of researchers. I’m sympathetic with them. They’ve been working with energy data for many years. They’re doing important work which is probably already difficult enough as it is. We respected their position and did not want to interfere with their research. My guess is that their research practices are actually largely consistent with what’s possible under differential privacy, but that’s an entirely separate discussion.
What’s tragic is that their opposition ended up hurting a consumer advocacy group who could’ve used differential privacy as a means to gain more access to energy data than they were able to get in the end (essentially nothing). There was a lot of miscommunication throughout the proceeding that clearly didn’t help. For instance, initially the consumer advocacy group proposed their own ad-hoc privacy solution (which we didn’t support). Only later did we find some common ground. In hindsight, we should’ve agreed on and jointly represented the same solution from the beginning. In my understanding, the use case didn’t require more than the kind of aggregate usage statistics that we could’ve easily produced while preserving differential privacy without any major engineering efforts.
Towards practicing differential privacy
Drawing on my experience with the CPUC case, I want to end with some concrete suggestions and questions hoping that they will help others when applying differential privacy. When I speak of “the community”, I will make some very broad generalizations knowing full well that in each instance there are certainly exceptions to what I claim. The discussion below is by no means a survey as it contains very few links to the rich literature on differential privacy. I strongly encourage you to fill in relevant missing pointers in the comments.
Focus on win-win applications
Apply differential privacy as a tool to provide access to data where currently access is problematic due to privacy regulations. Don’t fight the data analyst. Don’t play the moral police. Imagine you are the analyst.
As a privacy expert, you will find yourself having to shoot down inadequate solutions all the time. Why can’t we just omit those 18 sensitive attributes like in the HIPAA safe harbor provision? Why isn’t it safe to release any statistic that is aggregated over at least 15 households in which no single household contributes 15% of the total number (i.e., the “15/15” rule)? Such ad-hoc rules sound intuitively appealing to non-experts. Refuting them is time-consuming and makes you look defensive.
Rather than shooting down what doesn’t work, point out why differential privacy is better than those solutions not just from a privacy perspective but rather from a utility perspective. Unlike these solutions, differential privacy does not alter your data set at all. In particular, from a statistical perspective you do not change the distribution from which the data were drawn. This is an incredibly powerful proposition. I think that data analysis with differential privacy can be vastly more useful than what you get after applying, for instance, the HIPAA safe harbor mechanism.
My point is that there are many “win/win” applications of differential privacy where it simultaneously can give better utility and better privacy than its alternatives. As the CPUC case showed, sometimes the choice is even between no access at all and differentially private access. It’s really a no-brainer. We should start with such applications instead of arguing about completely unrestricted access versus differentially private access.
Don’t empower the naysayers
In my opinion, for differential privacy to be a major success in practice it would be sufficient if it were successful in some applications but certainly not in all—not even in most. There’s a culture of criticizing differential privacy based on the perfectly correct observation that some differentially private algorithm (say, Laplace) didn’t give enough utility in some application. These kind of observations—valid as they may be—say very little about the potential of differential privacy in practice. First of all, they only evaluate one algorithm while there could be much better algorithms. Second, they commit to one specific application and, more importantly, one particular modeling of the problem. Perhaps there’s a different approach to the same problem that’s more compatible with differential privacy. It’s simply impossible to rule out differential privacy as a solution through these kind of straw man experiments.
The differential privacy community is partially at blame for empowering the naysayers, since they have advertised differential privacy as a universal solution concept to the privacy problem. This is theoretically true in some sense, but the situation in practice is much more delicate. So, stop feeding the naysayers. Start presenting differential privacy as a promising technology for some applications but certainly not all.
Change your narrative
Don’t present differential privacy as a fear inducing crypto hammer designed to obfuscate data access. That’s not what it is. Differential privacy is a rigorous way of doing machine learning, not a way of preventing machine learning from being done. We understand perfectly well now that differential privacy is a stability guarantee which is fundamentally aligned with the central goal of statistics, namely, to learn from data about the population as a whole and not about specific individuals. This understanding perhaps wasn’t quite there in the beginning, but it is now. Academics should from time to time come up with a new page 1 for their papers.
Build reliable code repositories
A weakness of the differential privacy community has been the scarcity of available high quality code. There are many academic code pieces available by emailing someone, but we don’t have many visible repositories on github or elsewhere that provide robust implementations of common differentially private algorithms. Frank McSherry’s PINQ was a really wonderful step in the right direction, but it is no longer maintained and by now out of date. Written in C#, it hasn’t been easy for many to build on and extend PINQ. A more recent notable effort is the Dual Query code though it requires CPLEX to run.
What scares me a bit is that even a project as solidly designed and carefully executed as PINQ did not address low-level implementation issues such as floating point vulnerabilities in differential privacy.
I’m guilty myself. Many have used or tried to use MWEM, an algorithm Katrina Ligett, Frank McSherry and I presented at NIPS a few years ago. Yet we don’t have a great implementation publicly available. You can email us for a decent C# implementation (alas!), but instead a lot of people have produced their own implementations of our algorithm over the years. I regularly have the urge to start an open source project for it, but then I realize it’s a bit of a bottomless pit. In order to have a solid implementation of MWEM, I’d first need to have a solid implementation of all the primitives with all the low-level issues that come up. In any case, if somebody more brave then myself took the first step on an open source effort (preferably not in C#), I’d be very eager to contribute.
Taking a more modest step, I feel compelled to compile a list of available code repositories. If you have any pointers, please leave a comment!
Be less general and more domain-specific
Much of the academic research on differential privacy has focused on generality. That makes sense theoretically, but it means that reading the scientific literature on differential privacy from the point of view of a domain expert can be very frustrating. Most papers start with toy examples that make perfect sense on a theoretical level, but will appear alarmingly naïve to a domain expert.
The community is at a point where we need to transition from generality to specificity. For example, what’s needed are domain-specific tutorials that walk practitioners through real examples. One reason why such tutorials don’t exist is that they take a lot of time and writing them isn’t incentivized by academia. One way out of this is for journal editors and conferences to specifically invite such tutorials. Similarly, the community should at this point have very high regard for positive results and case studies in specific application domains even if they are limited in scope and don’t contribute technically new solutions.
Be more entrepreneurial
The CPUC case highlighted that the application of differential privacy in practice can fail as a result of many non-technical issues. These important issues are often not on the radar of academic researchers. We spent an awful lot of time talking about the technical strengths or limitations of differential privacy, while missing out on some very real challenges. It’s quite reasonable to argue that these challenges should be outside the scope of academia. On the other hand, academics are currently the only available experts on differential privacy and there’s obvious demand for it. Where should we draw the line?
To be blunt, I think an important ingredient that’s missing in the current differential privacy ecosystem is money. There is only so much that academic researchers can do to promote a technology. Beyond a certain point businesses have to commercialize the technology for it be successful. The CPUC case was much better suited as the full-time job for a group of paid professionals rather than a volunteering effort. I’m surprised none of the researchers working on differential privacy have devoted a sabbatical to running a privacy startup. It’s needed and the potential upside is big. Why not give it a shot? I hear tenured jobs are meant for running startups.
So, is differential privacy practical?
I like the answer Aaron Roth gave when I asked him: