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844
Privacy Preserving Association Rule Mining in Vertically Partitioned Data
- In The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2002
"... Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association ru ..."
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Cited by 295 (21 self)
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Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.
Robust De-anonymization of Large Sparse Datasets.
- IEEE Symposium on Security and Privacy
, 2008
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Revealing information while preserving privacy
- In PODS
, 2003
"... We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d1,.., dn, with a query being a subset q ⊆ [n] to be answered by � i∈q di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset ..."
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Cited by 272 (9 self)
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We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d1,.., dn, with a query being a subset q ⊆ [n] to be answered by � i∈q di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset sums. Applying this reconstruction algorithm to statistical databases we show that in order to achieve privacy one has to add perturbation of magnitude Ω ( √ n). That is, smaller perturbation always results in a strong violation of privacy. We show that this result is tight by exemplifying access algorithms for statistical databases that preserve privacy while adding perturbation of magnitude Õ(√n). For time-T bounded adversaries we demonstrate a privacy-preserving access algorithm whose perturbation magnitude is ≈ √ T. 1
Differential privacy: A survey of results
- In Theory and Applications of Models of Computation
, 2008
"... Abstract. Over the past five years a new approach to privacy-preserving ..."
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Cited by 258 (0 self)
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Abstract. Over the past five years a new approach to privacy-preserving
Transforming Data to Satisfy Privacy Constraints
, 2002
"... Data on individuals and entities are being collected widely. These data can contain information that explicitly identifies the individual (e.g., social security number). Data can also contain other kinds of personal information (e.g., date of birth, zip code, gender) that are potentially identifying ..."
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Cited by 250 (0 self)
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Data on individuals and entities are being collected widely. These data can contain information that explicitly identifies the individual (e.g., social security number). Data can also contain other kinds of personal information (e.g., date of birth, zip code, gender) that are potentially identifying when linked with other available data sets. Data are often shared for business or legal reasons. This paper addresses the important issue of preserving the anonymity of the individuals or entities during the data dissemination process. We explore preserving the anonymity by the use of generalizations and suppressions on the potentially identifying portions of the data. We extend earlier works in this area along various dimensions. First, satisfying privacy constraints is considered in conjunction with the usage for the data being disseminated. This allows us to optimize the process of preserving privacy for the specified usage. In particular, we investigate the privacy transformation in the context of data mining applications like building classification and regression models. Second, our work improves on previous approaches by allowing more flexible generalizations for the data. Lastly, this is combined with a more thorough exploration of the solution space using the genetic algorithm framework. These extensions allow us to transform the data so that they are more useful for their intended purpose while satisfying the privacy constraints.
Privacy-preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
, 2002
"... Data mining can extract important knowledge from large data collections -- but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data, and some types of information about the data. This paper addresses secure mining of ass ..."
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Cited by 240 (18 self)
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Data mining can extract important knowledge from large data collections -- but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data, and some types of information about the data. This paper addresses secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
An architecture for privacy-sensitive ubiquitous computing
- In MobiSYS ’04: Proceedings of the 2nd international conference on mobile systems, applications, and services
, 2004
"... Privacy is the most often-cited criticism of ubiquitous computing, and may be the greatest barrier to its long-term success. However, developers currently have little support in designing software architectures and in creating interactions that are effective in helping end-users manage their privacy ..."
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Cited by 231 (16 self)
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Privacy is the most often-cited criticism of ubiquitous computing, and may be the greatest barrier to its long-term success. However, developers currently have little support in designing software architectures and in creating interactions that are effective in helping end-users manage their privacy. To address this problem, we present Confab, a toolkit for facilitating the development of privacy-sensitive ubiquitous computing applications. The requirements for Confab were gathered through an analysis of privacy needs for both end-users and application developers. Confab provides basic support for building ubiquitous computing applications, providing a framework as well as several customizable privacy mechanisms. Confab also comes with extensions for managing location privacy. Combined, these features allow application developers and end-users to support a spectrum of trust levels and privacy needs.
Practical privacy: the sulq framework
- In PODS ’05: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
, 2005
"... We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping ..."
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Cited by 223 (35 self)
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We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is P i∈S f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise – much less than the sampling error – provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large. We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].
Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography
, 2007
"... In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a singl ..."
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Cited by 220 (2 self)
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In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.
