Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs. With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions. We also unify this approach with approximate differential privacy by giving an appropriate definition of "approximate concentrated differential privacy.

Catalyzing Privacy Law

The United States famously lacks a comprehensive federal data privacy law. In the past year, however, over half the states have proposed broad privacy bills or have established task forces to propose possible privacy legislation. Meanwhile, congressional committees are holding hearings on multiple privacy bills. What is catalyzing this legislative momentum? Some believe that Europe’s General Data Protection Regulation (GDPR), which came into force in 2018, is the driving factor. But with the California Consumer Privacy Act (CCPA) which took effect in January 2020, California has emerged as an alternate contender in the race to set the new standard for privacy.Our close comparison of the GDPR and California’s privacy law reveals that the California law is not GDPR-lite: it retains a fundamentally American approach to information privacy. Reviewing the literature on regulatory competition, we argue that California, not Brussels, is catalyzing privacy law across the United States. And what is happening is not a simple story of powerful state actors. It is more accurately characterized as the result of individual networked norm entrepreneurs, influenced and even empowered by data globalization. Our study helps explain the puzzle of why Europe’s data privacy approach failed to spur US legislation for over two decades. Finally, our study answers critical questions of practical interest to individuals—who will protect my privacy?—and to businesses—whose rules should I follow

Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals

We prove new positive and negative results concerning the existence of truthful and individually rational mechanisms for purchasing private data from individuals with unbounded and sensitive privacy preferences. We strengthen the impossibility results of Ghosh and Roth (EC 2011) by extending it to a much wider class of privacy valuations. In particular, these include privacy valuations that are based on ({\epsilon}, {\delta})-differentially private mechanisms for non-zero {\delta}, ones where the privacy costs are measured in a per-database manner (rather than taking the worst case), and ones that do not depend on the payments made to players (which might not be observable to an adversary). To bypass this impossibility result, we study a natural special setting where individuals have mono- tonic privacy valuations, which captures common contexts where certain values for private data are expected to lead to higher valuations for privacy (e.g. having a particular disease). We give new mech- anisms that are individually rational for all players with monotonic privacy valuations, truthful for all players whose privacy valuations are not too large, and accurate if there are not too many players with too-large privacy valuations. We also prove matching lower bounds showing that in some respects our mechanism cannot be improved significantly