Efficiently Querying Databases While Providing Differential Privacy
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From Local to Central Differential Privacy Via Anonymity
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar´ Erlingsson∗ Vitaly Feldman∗ Ilya Mironov∗ Ananth Raghunathan∗ Kunal Talwar∗ Abhradeep Thakurtay Abstract Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users’ private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user’s value. More fundamentally—by building on anonymity of the users’ reports—we also demonstrate how the privacy cost of our LDP algorithm can actually be much lower when viewed in the central model of dif- ferential privacy. We show, via a new and general privacy amplification technique, that any permutation- p invariant algorithm satisfying "-local differential privacy will satisfyp (O(" log(1/δ)=n); δ)-central dif- ferential privacy. By this, we explain how the high noise and n overhead of LDP protocols is a con- sequence of them being significantly more private in the central model. As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised " would indicate—at least if reports are anonymized. 1 Introduction A frequent task in data analysis is the monitoring of the statistical properties of evolving data in a manner that requires repeated computation on the entire evolving dataset. Software applications commonly apply online monitoring, e.g., to establish trends in the software configurations or usage patterns. -
The Limits of Post-Selection Generalization
The Limits of Post-Selection Generalization Kobbi Nissim∗ Adam Smithy Thomas Steinke Georgetown University Boston University IBM Research – Almaden [email protected] [email protected] [email protected] Uri Stemmerz Jonathan Ullmanx Ben-Gurion University Northeastern University [email protected] [email protected] Abstract While statistics and machine learning offers numerous methods for ensuring gener- alization, these methods often fail in the presence of post selection—the common practice in which the choice of analysis depends on previous interactions with the same dataset. A recent line of work has introduced powerful, general purpose algorithms that ensure a property called post hoc generalization (Cummings et al., COLT’16), which says that no person when given the output of the algorithm should be able to find any statistic for which the data differs significantly from the population it came from. In this work we show several limitations on the power of algorithms satisfying post hoc generalization. First, we show a tight lower bound on the error of any algorithm that satisfies post hoc generalization and answers adaptively chosen statistical queries, showing a strong barrier to progress in post selection data analysis. Second, we show that post hoc generalization is not closed under composition, despite many examples of such algorithms exhibiting strong composition properties. 1 Introduction Consider a dataset X consisting of n independent samples from some unknown population P. How can we ensure that the conclusions drawn from X generalize to the population P? Despite decades of research in statistics and machine learning on methods for ensuring generalization, there is an increased recognition that many scientific findings do not generalize, with some even declaring this to be a “statistical crisis in science” [14]. -
Individuals and Privacy in the Eye of Data Analysis
Individuals and Privacy in the Eye of Data Analysis Thesis submitted in partial fulfillment of the requirements for the degree of “DOCTOR OF PHILOSOPHY” by Uri Stemmer Submitted to the Senate of Ben-Gurion University of the Negev October 2016 Beer-Sheva This work was carried out under the supervision of Prof. Amos Beimel and Prof. Kobbi Nissim In the Department of Computer Science Faculty of Natural Sciences Acknowledgments I could not have asked for better advisors. I will be forever grateful for their close guidance, their constant encouragement, and the warm shelter they provided. Without them, this thesis could have never begun. I have been fortunate to work with Raef Bassily, Amos Beimel, Mark Bun, Kobbi Nissim, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan. I enjoyed working with them all, and I thank them for everything they have taught me. iii Contents Acknowledgments . iii Contents . iv List of Figures . vi Abstract . vii 1 Introduction1 1.1 Differential Privacy . .2 1.2 The Sample Complexity of Private Learning . .3 1.3 Our Contributions . .5 1.4 Additional Contributions . 10 2 Related Literature 15 2.1 The Computational Price of Differential Privacy . 15 2.2 Interactive Query Release . 18 2.3 Answering Adaptively Chosen Statistical Queries . 19 2.4 Other Related Work . 21 3 Background and Preliminaries 22 3.1 Differential privacy . 22 3.2 Preliminaries from Learning Theory . 24 3.3 Generalization Bounds for Points and Thresholds . 29 3.4 Private Learning . 30 3.5 Basic Differentially Private Mechanisms . 31 3.6 Concentration Bounds . 33 4 The Generalization Properties of Differential Privacy 34 4.1 Main Results . -
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space Adam Smith Shuang Song Abhradeep Thakurta Boston University Google Research, Brain Team Google Research, Brain Team [email protected] [email protected] [email protected] Abstract We revisit the problem of counting the number of distinct elements F0(D) in a data stream D, over a domain [u]. We propose an ("; δ)-differentially private algorithm that approximates F0(D) within a factor of (1 ± γ), and with additive error of p O( ln(1/δ)="), using space O(ln(ln(u)/γ)/γ2). We improve on the prior work at least quadratically and up to exponentially, in terms of both space and additive p error. Our additive error guarantee is optimal up to a factor of O( ln(1/δ)), n ln(u) 1 o and the space bound is optimal up to a factor of O min ln γ ; γ2 . We assume the existence of an ideal uniform random hash function, and ignore the space required to store it. We later relax this requirement by assuming pseudo- random functions and appealing to a computational variant of differential privacy, SIM-CDP. Our algorithm is built on top of the celebrated Flajolet-Martin (FM) sketch. We show that FM-sketch is differentially private as is, as long as there are p ≈ ln(1/δ)=(εγ) distinct elements in the data set. Along the way, we prove a structural result showing that the maximum of k i.i.d. random variables is statisti- cally close (in the sense of "-differential privacy) to the maximum of (k + 1) i.i.d. -
Calibrating Noise to Sensitivity in Private Data Analysis
Calibrating Noise to Sensitivity in Private Data Analysis Cynthia Dwork1, Frank McSherry1, Kobbi Nissim2, and Adam Smith3? 1 Microsoft Research, Silicon Valley. {dwork,mcsherry}@microsoft.com 2 Ben-Gurion University. [email protected] 3 Weizmann Institute of Science. [email protected] Abstract. We continue a line of research initiated in [10, 11] on privacy- preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = P i g(xi), where xi denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard devi- ation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation re- sults showing the increased value of interactive sanitization mechanisms over non-interactive. -
Differential Privacy, Property Testing, and Perturbations
Differential Privacy, Property Testing, and Perturbations by Audra McMillan A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Mathematics) in The University of Michigan 2018 Doctoral Committee: Professor Anna Gilbert, Chair Professor Selim Esedoglu Professor John Schotland Associate Professor Ambuj Tewari Audra McMillan [email protected] ORCID iD: 0000-0003-4231-6110 c Audra McMillan 2018 ACKNOWLEDGEMENTS First and foremost, I would like to thank my advisor, Anna Gilbert. Anna managed to strike the balance between support and freedom that is the goal of many advisors. I always felt that she believed in my ideas and I am a better, more confident researcher because of her. I was fortunate to have a large number of pseudo-advisors during my graduate ca- reer. Martin Strauss, Adam Smith, and Jacob Abernethy deserve special mention. Martin guided me through the early stages of my graduate career and helped me make the tran- sition into more applied research. Martin was the first to suggest that I work on privacy. He taught me that interesting problems and socially-conscious research are not mutually exclusive. Adam Smith hosted me during what became my favourite summer of graduate school. I published my first paper with Adam, which he tirelessly guided towards a pub- lishable version. He has continued to be my guide in the differential privacy community, introducing me to the right people and the right problems. Jacob Abernethy taught me much of what I know about machine learning. He also taught me the importance of being flexible in my research and that research is as much as about exploration as it is about solving a pre-specified problem. -
Amicus Brief of Data Privacy Experts
Case 3:21-cv-00211-RAH-ECM-KCN Document 99-1 Filed 04/23/21 Page 1 of 27 EXHIBIT A Case 3:21-cv-00211-RAH-ECM-KCN Document 99-1 Filed 04/23/21 Page 2 of 27 UNITED STATES DISTRICT COURT FOR THE MIDDLE DISTRICT OF ALABAMA EASTERN DIVISION THE STATE OF ALABAMA, et al., ) ) Plaintiffs, ) ) v. ) Civil Action No. ) 3:21-CV-211-RAH UNITED STATES DEPARTMENT OF ) COMMERCE, et al., ) ) Defendants. ) AMICUS BRIEF OF DATA PRIVACY EXPERTS Ryan Calo Deirdre K. Mulligan Ran Canetti Omer Reingold Aloni Cohen Aaron Roth Cynthia Dwork Guy N. Rothblum Roxana Geambasu Aleksandra (Seša) Slavkovic Somesh Jha Adam Smith Nitin Kohli Kunal Talwar Aleksandra Korolova Salil Vadhan Jing Lei Larry Wasserman Katrina Ligett Daniel J. Weitzner Shannon L. Holliday Michael B. Jones (ASB-5440-Y77S) Georgia Bar No. 721264 COPELAND, FRANCO, SCREWS [email protected] & GILL, P.A. BONDURANT MIXSON & P.O. Box 347 ELMORE, LLP Montgomery, AL 36101-0347 1201 West Peachtree Street, NW Suite 3900 Atlanta, GA 30309 Counsel for the Data Privacy Experts #3205841v1 Case 3:21-cv-00211-RAH-ECM-KCN Document 99-1 Filed 04/23/21 Page 3 of 27 TABLE OF CONTENTS STATEMENT OF INTEREST ..................................................................................... 1 SUMMARY OF ARGUMENT .................................................................................... 1 ARGUMENT ................................................................................................................. 2 I. Reconstruction attacks Are Real and Put the Confidentiality of Individuals Whose Data are Reflected -
Calibrating Noise to Sensitivity in Private Data Analysis
Calibrating Noise to Sensitivity in Private Data Analysis Cynthia Dwork1, Frank McSherry1, Kobbi Nissim2, and Adam Smith3? 1 Microsoft Research, Silicon Valley. {dwork,mcsherry}@microsoft.com 2 Ben-Gurion University. [email protected] 3 Weizmann Institute of Science. [email protected] Abstract. We continue a line of research initiated in [10, 11] on privacy- preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. P Previous work focused on the case of noisy sums, in which f = i g(xi), where xi denotes the ith row of the database and g maps data- base rows to [0, 1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speak- ing, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation re- sults showing the increased value of interactive sanitization mechanisms over non-interactive. -
Towards Formalizing the GDPR's Notion of Singling
Towards Formalizing the GDPR's Notion of Singling Out Aloni Cohen∗ Kobbi Nissim† December 21, 2020 Abstract There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to which technical offerings adequately match expectations expressed in legal standards. The uncertainty is exacerbated by a litany of successful privacy attacks demonstrating that traditional statistical disclosure limitation techniques often fall short of the sort of privacy envisioned by legal standards. We define predicate singling out, a new type of privacy attack intended to capture the concept of singling out appearing in the General Data Protection Regulation (GDPR). Informally, an adversary predicate singles out a dataset x using the output of a data-release mechanism M(x) if it manages to find a predicate p matching exactly one row x x with probability much better than a statistical baseline. A data-release mechanism that2 precludes such attacks is secure against predicate singling out (PSO secure). We argue that PSO security is a mathematical concept with legal consequences. Any data- release mechanism that purports to \render anonymous" personal data under the GDPR must be secure against singling out, and, hence, must be PSO secure. We then analyze PSO security, showing that it fails to self-compose. Namely, a combination of !(log n) exact counts, each individually PSO secure, enables an attacker to predicate single out. In fact, the composition of just two PSO-secure mechanisms can fail to provide PSO security. Finally, we ask whether differential privacy and k-anonymity are PSO secure. Leveraging a connection to statistical generalization, we show that differential privacy implies PSO security. -
Differentially Private Deep Learning with Smooth Sensitivity
Differentially Private Deep Learning with Smooth Sensitivity Lichao Sun Yingbo Zhou University of Illinois at Chicago Salesforce Research [email protected] [email protected] Philip S. Yu Caiming Xiong University of Illinois at Chicago Salesforce Research [email protected] [email protected] ABSTRACT KEYWORDS Ensuring the privacy of sensitive data used to train modern ma- privacy, differential privacy, smooth sensitivity, teacher-student chine learning models is of paramount importance in many areas learning, data-dependent analysis of practice. One approach to study these concerns is through the ACM Reference Format: lens of differential privacy. In this framework, privacy guarantees Lichao Sun, Yingbo Zhou, Philip S. Yu, and Caiming Xiong. 2020. Differ- are generally obtained by perturbing models in such a way that entially Private Deep Learning with Smooth Sensitivity. In KDD ’20: ACM specifics of data used to train the model are made ambiguous. A SIGKDD International Conference on Knowledge discovery and data min- particular instance of this approach is through a “teacher-student” ing, Aug 22–27, 2020, San Diego, CA. ACM, New York, NY, USA, 10 pages. framework, wherein the teacher, who owns the sensitive data, pro- https://doi.org/10.1145/1122445.1122456 vides the student with useful, but noisy, information, hopefully allowing the student model to perform well on a given task without access to particular features of the sensitive data. Because stronger 1 INTRODUCTION privacy guarantees generally involve more significant perturbation Recent years have witnessed impressive breakthroughs of deep on the part of the teacher, deploying existing frameworks funda- learning in a wide variety of domains, such as image classifica- mentally involves a trade-off between student’s performance and tion [20], natural language processing [8], and many more. -
The Gödel Prize 2020 - Call for Nominatonn
The Gödel Prize 2020 - Call for Nominatonn Deadline: February 15, 2020 The Gödel Prize for outntanding papern in the area of theoretial iomputer niienie in nponnored jointly by the European Annoiiaton for Theoretial Computer Siienie (EATCS) and the Annoiiaton for Computng Maihinery, Speiial Innterent Group on Algorithmn and Computaton Theory (AC M SInGACT) The award in prenented annually, with the prenentaton taaing plaie alternately at the Innternatonal Colloquium on Automata, Languagen, and Programming (InCALP) and the AC M Symponium on Theory of Computng (STOC) The 28th Gödel Prize will be awarded at the 47th Innternatonal Colloquium on Automata, Languagen, and Programming to be held during 8-12 July, 2020 in Beijing The Prize in named in honour of Kurt Gödel in reiogniton of hin major iontributonn to mathematial logii and of hin interent, diniovered in a leter he wrote to John von Neumann nhortly before von Neumann’n death, in what han beiome the famoun “P vernun NP” quenton The Prize iniluden an award of USD 5,000 Award Committee: The 2020 Award Commitee ionnintn of Samnon Abramnay (Univernity of Oxford), Anuj Dawar (Chair, Univernity of Cambridge), Joan Feigenbaum (Yale Univernity), Robert Krauthgamer (Weizmann Innnttute), Daniel Spielman (Yale Univernity) and David Zuiaerman (Univernity of Texan, Auntn) Eligibility: The 2020 Prize rulen are given below and they nupernede any diferent interpretaton of the generii rule to be found on webniten of both SInGACT and EATCS Any renearih paper or nerien of papern by a ningle author or by -
Smooth Sensitivity and Sampling in Private Data Analysis∗
Smooth Sensitivity and Sampling in Private Data Analysis∗ Kobbi Nissimy Sofya Raskhodnikovaz Adam Smithx Draft Full Version, v1.0 May 17, 2011 Abstract We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains. Our framework allows one to release functions f of the data with instance-specific additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also by the database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smooth sensitivity of f on the database x — a measure of variability of f in the neighborhood of the instance x. The new framework greatly expands the applicability of output perturbation, a technique for protecting individuals’ privacy by adding a small amount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-specific noise in the context of data privacy. Our framework raises many interesting algorithmic questions. Namely, to apply the framework one must compute or approximate the smooth sensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on many databases x.