After all, a person is herself, and
others. Relationships chisel the
final shape of one’s being. I am
me, and you.
(N.K. Jemisin, The Fifth Season)
Alice sends an email to her friends and family to organize her birthday party. Bob explains his symptoms to his doctor, who takes notes on her computer. Camille requests directions to a restaurant on their favorite navigation app. Dede puts on a fitness tracker and goes out for a run around the neighborhood. Ember goes for a hike, and takes a few pictures of the view from up the hill.
All these activities will generate a trail of data. Nowadays, few are the endeavors that do not leave a digital trace on a person’s devices, or on some remote server farm. This historically new phenomenon appeared and drastically expanded over the last few decades. Before that, only a few large industries and governments were automating computing and data processing. What brought about this extensive change? Improvements in hardware? A few core innovations, like the World Wide Web? Market forces?
Rather than looking for causes, one could look for incentives. Who benefits most from the collection, processing, and sharing of this data? Technology companies like to argue that their customers—or users—are the primary beneficiaries of their services. At first glance, the argument appears reasonable: many Internet services are free, and people use them because they find some value in them. Organizing one’s pictures, looking up information for a school assignment, staying in touch with loved ones, watching TV shows at any time, are all examples of benefits that people get by sharing their personal data with online services. Even people who are otherwise reluctant to technological change can usually find parts of their lives to be made easier or more convenient thanks to technological innovations of the 21st century.
Of course, despite what their founders like to pretend, tech companies are not providing these services out of the goodness of their heart. Even if they did, building, running, and maintaining large-scale online services is expensive. So what are tech companies getting out of the exchange? How do they transform all the data they collect into profit? And how fair is that exchange?
One of the common mechanisms to extract profit out of personal data is personalized advertising. The massive scale and complexity of the online advertising industry is difficult to grasp, but the basic principle of personalized ads is relatively simple. Consider a fitness tracking app, in which users record their performance when sports training. A sportswear company launching a new line of triathlon clothes might pay the app provider to advertise their product to users who recently recorded their performance in running, cycling and swimming.
In this example, an individual user might find the exchange reasonable and fair. They get the benefit of using the app for free, at the “cost” of seeing ads that are relevant to their interests. The app company earns money for each impression—when an ad is shown to users—and conversion—when a user clicks on an ad and ends up buying the product. The company revenue scales roughly linearly with the number of users: twice as many users translate to twice as many impressions and conversions.
This individual view, however, is very narrow: it only considers each piece of personal data in isolation. It neglects a crucial aspect: individual data has additional value in large quantities, as large datasets are more than the sum of their parts. An app developer might collect data to measure what features of their app are most popular among its users. Ad tech companies can run experiments and measure which kind of ads are most likely to lead to profitable conversions. Aggregate data can also be used to do market research, enabling marketers to discover consumer trends early and act on them before their competitors.
Such analyses are only possible if data from sufficiently many people are included in the dataset: the value of a dataset increases more than linearly with its size. This creates feedback loops, especially with the recent advances in machine learning. The more training data companies have, the better their machine learning models perform, and the more useful their products become, which encourages more people to use them, which in turn increases their data and revenue. In parallel, as their user base grows, their advertising business becomes more efficient and profitable, giving a further advantage to large, established tech companies.
Seen under this lens, this phenomenon seems hardly fair. Why do individuals only get the benefits from the processing of their individual data, while large companies profit from the added, “compound” benefits of large datasets? Public relations departments of tech companies typically have two answers to this objection.
First, they point out that data collection can help improve the product itself, which benefits users. Examples abound. A search engine can get better over time, as its algorithm gets updated to improve accuracy or user satisfaction metrics. Data collection from the users of a navigation app enables more accurate traffic predictions, which in turns enables faster and smarter routing. A music app can aggregate the data it collects to improve its recommendation algorithm, which can help users discover their next favorite artist. To summarize the argument: sometimes, the incentives of tech companies and of their users are aligned, in which case large-scale data collection and processing can have an overall positive impact. Notice that this does not answer the fairness objection. If a company uses my data for five distinct purposes, and I only benefit from one of them, it is better than nothing, but the exchange still does not seem very fair.
This leads to the tech industry’s second answer to this objection: the development of initiatives focusing on using data “for good”. For instance, giving external researchers access to data for scientific research, forecasting the spread of diseases, developing early warning systems for natural disasters, etc. The idea behind these projects is to “give back” to the community: using the compound benefits of aggregated data for a cause that serves everyone. The scale of these efforts, however, is relatively small compared to other areas of business for tech companies.
For some efforts—especially when they require building entire systems from scratch, like early warning systems for earthquakes—this is not surprising. The incentives of a capitalist system do not exactly encourage companies to spend more time on doing social good than on pursuing their core business, or other profitable endeavors.
Some other efforts, though, seem to require little effort, and can potentially lead to significant positive outcomes for society and for the company’s public image. One major example is sharing data with external researchers. At first glance, it seems like this practice benefits all the actors involved. Academics get access to valuable data to study, allowing them to publish papers in prestigious venues and boost their careers. Society benefits from scientific progress brought about by this work. And the company can reap public image benefits for free: all the hard analysis work, requiring valuable time and expertise, is outsourced to researchers. Even better, the process improves relations with prominent academics, who are useful allies—key opinion formers, in public relations jargon—to have when trying to influence regulators or polish one’s media coverage.
So why is this kind of partnerships not more common? The main reason is risk. Sharing data externally, even under non-disclosure agreements and with some security measures in place, increases the chances that it will be leaked to other third-parties, or used for unplanned ill-intentioned purposes. The best example of this phenomenon might be the Cambridge Analytica scandal. Aleksandr Kogan, who developed a Facebook app that collected the data of millions of users, was a Cambridge researcher and a Facebook consultant. The collected data was then used to target ads to individual voters and influence elections.
Concerns about privacy risks are therefore an understandable reason, as well as a convenient excuse, for companies to avoid sharing data externally. One of the ways to solve this problem, and re-align incentives, is anonymization. Anonymizing personal data means altering it to minimize or eliminate some of the privacy risks it carries. Of course, anonymization alone is pointless: redacting a dataset entirely is a very easy and effective way to remove all the risk associated with it, but it would also destroy its value completely. The core challenge of anonymization is thus to preserve some useful properties of data, while still limiting risk. In the example above, where a company wants to share data with researchers, the goal is to retain the statistical properties of the data that enable rigorous scientific analysis.
This is the fundamental challenge that this thesis focuses on. How to anonymize a dataset in a way that provides strong privacy guarantees, while still preserving its usefulness? The problems that this work investigates all derive from this core question. Which concepts and techniques already exist in the scientific literature? What are their limitations and the main obstacles to their adoption? And how can we best address them?
Today, anonymization is largely seen as something extremely complicated to achieve. People might have heard of the numerous anonymization failures of the past 21st century, and concluded that it is simply impossible to correctly anonymize data. Others might have heard of more recent, stronger definitions of anonymity like differential privacy—a core concept examined and used throughout this thesis—but dismiss it as purely theoretic, and too difficult to be used in practice. The goal of this work is to dispel these misconceptions, and make it easier and cheaper to safely anonymize data.
We start this ambitious goal in Chapter 2, by trying to determine what it means for data to be sufficiently anonymized. This is not a simple endeavor, as more than 200 variants and extensions of differential privacy have been introduced in the last decade. We systematize and categorize these notions, to provide a unified and simplified view of this field (Section 2.2). Then, in Chapter 3, we focus on one family of such variants: definitions that, in contrast to the original formulation of differential privacy, assume that the attacker only has partial knowledge about the input dataset. We identify and solve definitional problems in existing variants (Section 3.1), we present new and improved results on the privacy of common mechanisms (Section 3.2), and we prove impossibility results for the important problem of cardinality estimation (Section 3.3). Finally, in Chapter 4, we look at the main obstacles to the widespread use of anonymization techniques for practical problems. We propose scalable sketching algorithms for estimating reidentifiability and joinability risk (Section 4.1), as well as a framework to implement differential privacy as part of a query engine (Section 4.2). We then propose a number of possible improvements to such a system, and discuss some operational challenges raised by the use of differential privacy in practical scenarios (Section 4.3).