Open AccessJournal Article
Privacy-preserving datamining on vertically partitioned databases
Cynthia Dwork,Kobbi Nissim +1 more
TLDR
In this paper, Dinur and Nissim considered a statistical database in which a trusted database administrator monitors queries and introduces noise to the responses with the goal of maintaining data privacy, and they proved that unless the total number of queries is sublinear in the size of the database, a substantial amount of noise is required to avoid a breach, rendering the database almost useless.Abstract:
In a recent paper Dinur and Nissim considered a statistical database in which a trusted database administrator monitors queries and introduces noise to the responses with the goal of maintaining data privacy [5]. Under a rigorous definition of breach of privacy, Dinur and Nissim proved that unless the total number of queries is sub-linear in the size of the database, a substantial amount of noise is required to avoid a breach, rendering the database almost useless. As databases grow increasingly large, the possibility of being able to query only a sub-linear number of times becomes realistic. We further investigate this situation, generalizing the previous work in two important directions: multi-attribute databases (previous work dealt only with single-attribute databases) and vertically partitioned databases, in which different subsets of attributes are stored in different databases. In addition, we show how to use our techniques for datamining on published noisy statistics.read more
Citations
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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.
Book
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork,Aaron Roth +1 more
TL;DR: The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example.
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.
Book ChapterDOI
Differential privacy: a survey of results
TL;DR: This survey recalls the definition of differential privacy and two basic techniques for achieving it, and shows some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.
Journal ArticleDOI
Privacy-preserving data publishing: A survey of recent developments
TL;DR: This survey will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish P PDP from other related problems, and propose future research directions.
References
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Proceedings ArticleDOI
On the design and quantification of privacy preserving data mining algorithms
Dakshi Agrawal,Charu C. Aggarwal +1 more
TL;DR: It is proved that the EM algorithm converges to the maximum likelihood estimate of the original distribution based on the perturbed data, and proposed metrics for quantification and measurement of privacy-preserving data mining algorithms are proposed.
Journal ArticleDOI
Security-control methods for statistical databases: a comparative study
Nabil R. Adam,John C. Worthmann +1 more
TL;DR: This paper recommends directing future research efforts toward developing new methods that prevent exact disclosure and provide statistical-disclosure control, while at the same time do not suffer from the bias problem and the 0/1 query-set-size problem.
Proceedings ArticleDOI
Revealing information while preserving privacy
Irit Dinur,Kobbi Nissim +1 more
TL;DR: A polynomial reconstruction algorithm of data from noisy (perturbed) subset sums and shows that in order to achieve privacy one has to add perturbation of magnitude (Ω√n).
Proceedings ArticleDOI
Limiting privacy breaches in privacy preserving data mining
TL;DR: This paper presents a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them, and instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution.
Book ChapterDOI
Probabilistic encryption & how to play mental poker keeping secret all partial information
Shafi Goldwasser,Silvio Micali +1 more
TL;DR: In this article, the authors proposed an encryption scheme that is secure from an adversary who knows the encryption algorithm and is given the cyphertext, but cannot obtain any information about the clear-text.