Results 1 - 10 of 10,485

Data privacy through optimal k-anonymization

by Roberto J. Bayardo - In ICDE , 2005
"... Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the proper ..."
Abstract - Cited by 344 (3 self) - Add to MetaCart
Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has

Privacy-Preserving Data Mining

by Rakesh Agrawal , Ramakrishnan Srikant , 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
Abstract - Cited by 844 (3 self) - Add to MetaCart
A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models

Privacy Preserving Data Mining

by Yehuda Lindell, Benny Pinkas - JOURNAL OF CRYPTOLOGY , 2000
"... In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated b ..."
Abstract - Cited by 525 (9 self) - Add to MetaCart
In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated

No Free Lunch in Data Privacy

by Daniel Kifer, Ashwin Machanavajjhala
"... Differential privacy is a powerful tool for providing privacypreserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers changes very little with the addition or deletion of any tuple. It is frequently accompanied by popularized claims that i ..."
Abstract - Cited by 78 (6 self) - Add to MetaCart
that it provides privacy without any assumptions about the data and that it protects against attackers who know all but one record. In this paper we critically analyze the privacy protections offered by differential privacy. First, we use a no-free-lunch theorem, which defines nonprivacy as a game, to argue

Distributed Protocols for Data Privacy

by Zhiqiang Yang , 2007
"... With the rapid development of the Internet and computer technology, more and more of our activities are carried out on the Internet. Consequently, more and more data related to individuals are collected and used by different parties who are generally distributed over a wide variety of sites. Therefo ..."
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. Therefore, the protection of data privacy in such distributed settings is drawing more attention than ever. In this thesis, we present five techniques for protecting data privacy in different distributed settings by using cryptographic tools. For each of our techniques, we formally give an appro

Boosting and differential privacy

by Cynthia Dwork, Guy N. Rothblum, Salil Vadhan , 2010
"... Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacy-preserving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates of the resp ..."
Abstract - Cited by 648 (14 self) - Add to MetaCart
Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacy-preserving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates

A Data Privacy Taxonomy

by Ken Barker, Mina Askari, Mishtu Banerjee, Kambiz Ghazinour, Brenan Mackas, Maryam Majedi, Sampson Pun, Adepele Williams - In Lecture Notes in Computer Science: Dataspace: The Final Frontier , 2009
"... Abstract. Privacy has become increasingly important to the database commu-nity which is reflected by a noteworthy increase in research papers appearing in the literature. While researchers often assume that their definition of “privacy ” is universally held by all readers, this is rarely the case; s ..."
Abstract - Cited by 5 (2 self) - Add to MetaCart
; so many papers addressing key challenges in this domain have actually produced results that do not consider the same problem, even when using similar vocabularies. This paper provides an explicit definition of data privacy suitable for ongoing work in data repositories such as a DBMS or for data

Data Privacy: Definitions and Techniques

by Sabrina De Capitani di Vimercati, Sara Foresti, Giovanni Livraga, Pierangela Samarati - INTERNATIONAL JOURNAL OF UNCERTAINTY, FUZZINESS AND KNOWLEDGE-BASED SYSTEMS , 2012
"... The proper protection of data privacy is a complex task that requires a careful analysis of what actually has to be kept private. Several definitions of privacy have been proposed over the years, from traditional syntactic privacy definitions, which capture the protection degree enjoyed by data resp ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
The proper protection of data privacy is a complex task that requires a careful analysis of what actually has to be kept private. Several definitions of privacy have been proposed over the years, from traditional syntactic privacy definitions, which capture the protection degree enjoyed by data

k-anonymity: a model for protecting privacy.

by Latanya Sweeney - International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, , 2002
"... Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees ..."
Abstract - Cited by 1313 (15 self) - Add to MetaCart
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific

Maintaining Data Privacy in Association Rule Mining

by Shariq Rizvi, Jayant R. Haritsa - In Proceedings of the 28th VLDB Conference, Hong Kong , 2002
"... Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. We investigate here, with respect to mining association rules, whether users can be encouraged to provide correct information by ensuri ..."
Abstract - Cited by 171 (5 self) - Add to MetaCart
Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. We investigate here, with respect to mining association rules, whether users can be encouraged to provide correct information
Results 1 - 10 of 10,485