Our data, ourselves: privacy via distributed noise generation
Cynthia Dwork,Krishnaram Kenthapadi,Frank McSherry,Ilya Mironov,Moni Naor +4 more
- Vol. 4004, pp 486-503
TLDR
In this paper, a distributed protocol for generating shares of random noise, secure against malicious participants, was proposed, where the purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers.Abstract:
In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers [14,4,13]. In these databases, privacy is obtained by perturbing the true answer to a database query by the addition of a small amount of Gaussian or exponentially distributed random noise. The computational power of even a simple form of these databases, when the query is just of the form ∑if(di), that is, the sum over all rows i in the database of a function f applied to the data in row i, has been demonstrated in [4]. A distributed implementation eliminates the need for a trusted database administrator.
The results for noise generation are of independent interest. The generation of Gaussian noise introduces a technique for distributing shares of many unbiased coins with fewer executions of verifiable secret sharing than would be needed using previous approaches (reduced by a factor of n). The generation of exponentially distributed noise uses two shallow circuits: one for generating many arbitrarily but identically biased coins at an amortized cost of two unbiased random bits apiece, independent of the bias, and the other to combine bits of appropriate biases to obtain an exponential distribution.read more
Citations
More filters
Posted Content
Decentralized Detection with Robust Information Privacy Protection
Meng Sun,Wee Peng Tay +1 more
TL;DR: In this article, the authors consider a decentralized detection network where the raw sensor observations also allow the fusion center to infer private hypotheses that they wish to protect, and develop local privacy mappings at every sensor so that the sanitized sensor information minimizes the Bayes error of detecting the public hypothesis at the fusion centre, while achieving information privacy for all private hypotheses.
Posted Content
Optimizing Fitness-For-Use of Differentially Private Linear Queries
TL;DR: This paper designs a fitness-for-use strategy that adds privacy-preserving Gaussian noise to query answers that is optimized to meet the fine-grained accuracy requirements while minimizing the cost to privacy.
Journal ArticleDOI
Concentration Bounds for High Sensitivity Functions Through Differential Privacy
Uri Stemmer,Kobbi Nissim +1 more
TL;DR: In this paper, the authors show how differential privacy can be used as a mathematical tool for guaranteeing generalization in adaptive data analysis, where a differentially private analysis is applied on a sample S of i.i.p. examples to select a low-sensitivity function f.
Posted Content
Is privacy compatible with truthfulness
TL;DR: In this paper, the authors apply the notion of truthfulness from game theory to the problem of privacy-preserving data mining, and show that external incentives are necessary for people to participate in databases, and so data release mechanisms should not only be differentially private but also compatible with incentives.
Simultaneous discrimination prevention and privacy protection in data publishing and mining
TL;DR: This thesis investigates for the first time the problem of discrimination and privacy aware frequent pattern discovery and investigates the sanitization of the collection of patterns mined from a transaction database in such a way that neither privacy-violating nor discriminatory inferences can be inferred on the released patterns.
References
More filters
Book ChapterDOI
Calibrating noise to sensitivity in private data analysis
TL;DR: In this article, the authors show that for several particular applications substantially less noise is needed than was previously understood to be the case, and also show the separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Journal ArticleDOI
The Byzantine Generals Problem
TL;DR: The Albanian Generals Problem as mentioned in this paper is a generalization of Dijkstra's dining philosophers problem, where two generals have to come to a common agreement on whether to attack or retreat, but can communicate only by sending messengers who might never arrive.
Book ChapterDOI
The Byzantine generals problem
TL;DR: In this article, a group of generals of the Byzantine army camped with their troops around an enemy city are shown to agree upon a common battle plan using only oral messages, if and only if more than two-thirds of the generals are loyal; so a single traitor can confound two loyal generals.
Journal Article
Calibrating noise to sensitivity in private data analysis
TL;DR: The study is extended 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, which is the amount that any single argument to f can change its output.
Proceedings ArticleDOI
How to play ANY mental game
TL;DR: This work presents a polynomial-time algorithm that, given as a input the description of a game with incomplete information and any number of players, produces a protocol for playing the game that leaks no partial information, provided the majority of the players is honest.