Privacy Preserving Data Mining

Lindell, Yehuda; Pinkas, Benny

doi:10.1007/3-540-44598-6_3

Cited by 849 publications

(655 citation statements)

References 15 publications

(27 reference statements)

Supporting

Mentioning

650

Contrasting

Unclassified

Order By: Relevance

“…For instance, a notion of "data utility" has been introduced that characterizes the accuracy of a statistical function that can be evaluated from the released data (e.g., see Lindell and Pinkas (2000), Karr, Kohnen, Oganian, Reiter, and Sanil (2006), Brickell and Shmatikov (2008), Woo, Reiter, Oganian, and Karr (2009)), and it was found that existing data protection approaches lead to a decreasing quality of inference from the data measured in terms of this utility.…”

Section: Related Literaturementioning

confidence: 99%

Identification, Data Combination and the Risk of Disclosure

2014

View full text Add to dashboard Cite

It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual-level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined data set. We demonstrate the conditions on the observable marginal distributions of data in individual data sets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in a semiparametric setting such as ours. Provided that the (nonparametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a "de-anonymization" of the previously "anonymized" information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined data set allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire data set, and thus point inference requires the use of the 396 Komarova, Nekipelov, and Yakovlev Quantitative Economics 9 (2018) most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the "disclosive" nature of policies implemented by the datadriven companies (such as internet services companies and medical companies using individual patient records for policy decisions).

show abstract

Section: Related Literaturementioning

confidence: 99%

Identification, Data Combination and the Risk of Disclosure

2014

View full text Add to dashboard Cite

show abstract

“…Many algorithms and protocols have been introduced for these methods such as Clustering [1]- [7], classification [8]- [18], and association rule mining [19]- [26].…”

Section: A Non-incremental Algorithmsmentioning

confidence: 99%

New Incremental Privacy-Preserving Clustering Protocols

Samet¹,

Miri²

2013

LNSE

View full text Add to dashboard Cite

Abstract-We consider the problem of data clustering on streamed data, when the number of transactions is growing very quickly, or when data is distributed among several parties and their privacy is a concern. In this paper we present two new protocols for incremental privacy-preserving k-means clustering, which is a very popular data mining method, when data is distributed, horizontally or vertically, among multiple parties. At the end of each protocol, each party, without revealing its own private data, receives the final result of the clustering algorithm. Also, to improve efficiency, previous knowledge is used to incrementally update the centers and membership of each cluster.Index Terms-Clustering, security and privacy-preserving, incremental algorithms, data mining and machine learning, distributed data structures.

show abstract

“…However the process of mining data can result in violations of privacy. So, issues of privacy preservation in data mining are receiving more and more attention from the public [5] and many privacy preserving data mining approaches have been proposed for tackling the problem of privacy violation [17], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

“…The methods based on perturbation (e.g., [1], [3], [27]) are very efficient, but have a tradeoff between privacy and accuracy. The methods based on cryptography (e.g., [11], [23], [25]) can safely preserve privacy without loss of accuracy, but have high complexity and communication cost. These privacy preserving data mining methods have been presented for various scenarios in which the general idea is to allow mining datasets distributed across multiple parties, without disclosing each party's private data [2].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

2010

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

The Dung LUONG †a) , Nonmember and Tu Bao HO † †b) , Member SUMMARYRecently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well. key words: privacy preserving frequency mining, 2-part fully distributed setting, cryptography

show abstract

Privacy Preserving Data Mining

Cited by 849 publications

References 15 publications

Identification, Data Combination and the Risk of Disclosure

Identification, Data Combination and the Risk of Disclosure

New Incremental Privacy-Preserving Clustering Protocols

Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

Contact Info

Product

Resources

About