Defining Privacy and Utility in Data Sets

Is it possible to release useful data while preserving the privacy of the individuals whose information is in the database? This question has been the subject of considerable controversy, particularly in the wake of well-publicized instances in which researchers showed how to re-identify individuals in supposedly anonymous data. Some have argued that privacy and utility are fundamentally incompatible, while others have suggested that simple steps can be taken to achieve both simultaneously. Both sides have looked to the computer science literature for support. What the existing debate has overlooked, however, is that the relationship between privacy and utility depends crucially on what one means by “privacy” and what one means by “utility.” Apparently contradictory results in the computer science literature can be explained by the use of different definitions to formalize these concepts. Without sufficient attention to these definitional issues, it is all too easy to overgeneralize the technical results. More importantly, there are nuances to how definitions of “privacy” and “utility” can differ from each other, nuances that matter for why a definition that is appropriate in one context may not be appropriate in another. Analyzing these nuances exposes the policy choices inherent in the choice of one definition over another and thereby elucidates decisions about whether and how to regulate data privacy across varying social contexts

Commercial Speech Protection as Consumer Protection

The Supreme Court has long said that “the extension of First Amendment protection to commercial speech is justified principally by the value to consumers of the information such speech provides.” In other words, consumers—the recipients or listeners of commercial speech—are the ones the doctrine is meant to protect. In previous work, I explored the implications of taking this view seriously in three contexts: compelled speech, speech among commercial entities, and unwanted marketing. In each of those contexts, adopting a listener-oriented approach leads to the conclusion that many forms of commercial speech regulation should receive far less First Amendment scrutiny than most courts have given them. In that earlier work, I distinguished those forms of regulation from the more classic case of a regulation that directly prohibits or restricts some form of commercial communication to consumers. This Essay tackles that case. What if we reimagined commercial speech protection as a form of consumer protection, or at least as a doctrine aligned with consumer protection rather than opposed to it? That would mean that when government regulations do not impinge on the information available to consumer-listeners, courts should not apply the same kind of heightened scrutiny that they do when consumer listeners are being kept in the dark, even if those regulations may harm the interests of the commercial speakers. Commercial speech doctrine cares primarily about informing consumers, and that is the lens through which courts should determine how much scrutiny to give to a commercial speech restriction. In commercial speech cases, courts should not be applying the kind of speaker-focused approaches they would be using in cases involving noncommercial speech. This Essay begins by briefly reviewing the doctrinal and theoretical support for the proposition that commercial speech doctrine is about protecting consumers. Then, using the exaple of state no-surcharge laws (which generally prohibit charging customers more to use a credit card but permit cash discounts), I will argue that laws such as these that do not restrict the information available to consumers should not be subject to heightened scrutiny under the First Amendment. Finally, I will discuss the broader implications of this perspective for other laws that regulate the framing of consumer information and thus regulate consumer “nudges.

Defining Privacy and Utility in Data Sets

Is it possible to release useful data while preserving the privacy of the individuals whose information is in the database? This question has been the subject of considerable controversy, particularly in the wake of well-publicized instances in which researchers showed how to re-identify individuals in supposedly anonymous data. Some have argued that privacy and utility are fundamentally incompatible, while others have suggested that simple steps can be taken to achieve both simultaneously. Both sides have looked to the computer science literature for support. What the existing debate has overlooked, however, is that the relationship between privacy and utility depends crucially on what one means by privacy and what one means by utility. Apparently contradictory results in the computer science literature can be explained by the use of different definitions to formalize these concepts. Without sufficient attention to these definitional issues, it is all too easy to overgeneralize the technical results. More importantly, there are nuances to how definitions of privacy and utility can differ from each other, nuances that matter for why a definition that is appropriate in one context may not be appropriate in another. Analyzing these nuances exposes the policy choices inherent in the choice of one definition over another and thereby elucidates decisions about whether and how to regulate data privacy across varying social context

Adaptive Reduced Rank Regression

We study the low rank regression problem $\mathbf{y} = M\mathbf{x} + \epsilon$, where $\mathbf{x}$ and $\mathbf{y}$ are $d_1$ and $d_2$ dimensional vectors respectively. We consider the extreme high-dimensional setting where the number of observations $n$ is less than $d_1 + d_2$. Existing algorithms are designed for settings where $n$ is typically as large as $\mathrm{rank}(M)(d_1+d_2)$. This work provides an efficient algorithm which only involves two SVD, and establishes statistical guarantees on its performance. The algorithm decouples the problem by first estimating the precision matrix of the features, and then solving the matrix denoising problem. To complement the upper bound, we introduce new techniques for establishing lower bounds on the performance of any algorithm for this problem. Our preliminary experiments confirm that our algorithm often out-performs existing baselines, and is always at least competitive.Comment: 40 page