Open AccessBook Chapter10.1007/11530084_19

Performance-oriented privacy-preserving data integration

- 20 Jul 2005

- pp 240-256

TL;DR: The uncertainty introduced by collisions caused by hashing and the injection of noise can be leveraged to perform a privacy-preserving relational join operation between a massive public table and a relatively smaller private one.

Abstract: Current solutions to integrating private data with public data have provided useful privacy metrics, such as relative information gain, that can be used to evaluate alternative approaches. Unfortunately, they have not addressed critical performance issues, especially when the public database is very large. The use of hashes and noise yields better performance than existing techniques, while still making it difficult for unauthorized entities to distinguish which data items truly exist in the private database. As we show here, the uncertainty introduced by collisions caused by hashing and the injection of noise can be leveraged to perform a privacy-preserving relational join operation between a massive public table and a relatively smaller private one.

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1. What have the authors stated for future works in "Performance-oriented privacy-preserving data integration" ?

From their initial results presented, several future research directions can be pursued.. In future research, several additional features, such as the distribution of table S, should be incorporated into a new estimate.. Another future research direction is the use of a Bloom filter to reduce the size of the set R used by the hash/noise method.. If infinite domains are used, their method may be too conservative since there are an infinite number of actual values that may hash to a given hash value

2. What contributions have the authors mentioned in the paper "Performance-oriented privacy-preserving data integration" ?

The use of hashes and noise yields better performance than existing techniques while still making it difficult for unauthorized entities to distinguish which data items truly exist in the private database.. As the authors show here, leveraging the uncertainty introduced by collisions caused by hashing and the injection of noise, they present a technique for performing a relational join operation between a massive public table and a relatively smaller private one.

Figure 2. Privacy-preserving distributed join.

Figure 8. Hash sizes for variable |R|/|U|. Target prel = 0.01 and ch/ct = ½.

Figure 9. Varying normalized transmission costs wit h respect to the brute-force method. (a) Target prel = 0.01 and sh/st = ½. (b) |R|/|U| = 0.1 and ch/ct = ½.

Figure 6. Set sizes |N| and |F| for variable target prel. |R|/|U| = 0.1 and ch/ct = ½.

Figure 7. Varying absolute privacy. Target prel = 0.01 and ch/ct = ½.

Citations

•Posted Content

Protecting Data Privacy in Private Information Retrieval Schemes.

Yuval Ishai, +1 more

- 01 Jan 1997

- IACR Cryptology ePrint Archive

TL;DR: This paper shows how to transform PIR schemes into SPIR schemes (with information-theoretic privacy), paying a constant factor in communication complexity, and introduces a new cryptographic primitive, called conditional disclosure of secrets, which it is believed may be a useful building block for the design of other cryptographic protocols.

...read moreread less

519

Book Chapter•10.1007/978-3-642-33536-5_20

BLIP: non-interactive differentially-private similarity computation on bloom filters

Mohammad Alaggan, +2 more

- 01 Oct 2012

TL;DR: This paper proposes a novel non-interactive differentially private mechanism called BLIP (for BLoom-and-flIP) for randomizing Bloom filters and provides an analysis of the protection offered by BLIP against this profile reconstruction attack by deriving an upper and lower bound for the required value of the differential privacy parameter.

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•Journal Article

On addressing efficiency concerns in privacy-preserving mining

Shipra Agrawal, +2 more

- 01 Jan 2004

- Lecture Notes in Computer Science

TL;DR: In this article, the authors proposed a data distortion scheme for association rule mining that simultaneously provides both privacy to the user and accuracy in the mining results, and demonstrated that by generalizing the distortion process to perform symbol-specific distortion, appropriately choosing the distortion parameters, and applying a variety of optimizations in the reconstruction process, runtime efficiencies that are well within an order of magnitude of undistorted mining can be achieved.

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•Book Chapter•10.1007/978-3-540-87471-3_25

A Privacy-Preserving Framework for Integrating Person-Specific Databases

Murat Kantarcioglu, +2 more

- 24 Sep 2008

TL;DR: This paper develops protocols that enable data holders to merge personal records, thus creating larger profiles and diminishing duplication, and presents an extension to the protocol that permits the revelation of k-anonymous demographics, such that the administrator can perform joins more efficiently.

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Journal Article•10.1016/J.DATAK.2009.06.011

Formal anonymity models for efficient privacy-preserving joins

Murat Kantarcioglu, +3 more

- 01 Nov 2009

TL;DR: An extended version of the protocol is introduced in which data holders append k-anonymous features of their consumers to their encrypted submissions, which facilitate a more efficient join computation, while providing a formal guarantee that each record is linkable to no less than k individuals in the union of all organizations' consumers.

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A mathematical theory of communication

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Journal Article•10.1145/335191.335438

Privacy-preserving data mining

Rakesh Agrawal, +1 more

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TL;DR: This work considers the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed and proposes a novel reconstruction procedure to accurately estimate the distribution of original data values.

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Proceedings Article•10.1109/SFCS.1995.492461

Private information retrieval

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TL;DR: Schemes that enable a user to access k replicated copies of a database and privately retrieve information stored in the database and get no information on the identity of the item retrieved by the user are described.

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Journal Article•10.1145/293347.293350

Private information retrieval

Benny Chor, +3 more

- 01 Nov 1998

- Journal of the ACM

TL;DR: This work describes schemes that enable a user to access k replicated copies of a database and privately retrieve information stored in the database, so that each individual server gets no information on the identity of the item retrieved by the user.

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Performance-oriented privacy-preserving data integration

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Most frequently asked questions

1. What have the authors stated for future works in "Performance-oriented privacy-preserving data integration" ?

2. What contributions have the authors mentioned in the paper "Performance-oriented privacy-preserving data integration" ?

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Citations

Protecting Data Privacy in Private Information Retrieval Schemes.

BLIP: non-interactive differentially-private similarity computation on bloom filters

On addressing efficiency concerns in privacy-preserving mining

A Privacy-Preserving Framework for Integrating Person-Specific Databases

Formal anonymity models for efficient privacy-preserving joins

References

A mathematical theory of communication

Handbook of Applied Cryptography

Privacy-preserving data mining

Private information retrieval

Private information retrieval

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