Privacy Preserving Data Mining

@article{Lindell2001PrivacyPD,
  title={Privacy Preserving Data Mining
},
  author={Yehuda Lindell and Benny Pinkas},
  journal={Journal of Cryptology},
  year={2001},
  volume={15},
  pages={177-206}
}
Abstract. 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 by the need both to protect privileged information and to enable its use for research or other purposes. The above problem is a specific example of secure multi-party computation and, as such… Expand
Concept of Privacy protection in Data Mining
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 onExpand
Privacy Preservation of Sensitive Data used in Datamining Task
TLDR
This work considers a scenario in which party owning confidential database wish to run a data mining algorithm on their database, without revealing any unnecessary information, and proposes a technique to maintain privacy and also partial recovery of numerical attribute is possible. Expand
Privacy preserving data mining over vertically partitioned data
TLDR
This thesis argues that it is indeed possible to have efficient and practical techniques for useful privacy-preserving mining of knowledge from large amounts of data and presents several privacy preserving data mining algorithms operating over vertically partitioned data. Expand
Privacy preserving Data Mining Algorithms without the use of Secure Computation or Perturbation
  • Alex Gurevich, E. Gudes
  • Computer Science
  • 2006 10th International Database Engineering and Applications Symposium (IDEAS'06)
  • 2006
TLDR
A new paradigm to perform privacy-preserving distributed data mining without using the perturbation method and the secure computation method is offered, and three algorithms for association rule mining which use this paradigm are presented and discussed. Expand
Privacy Preserving Data Mining by Cyptography
TLDR
This work describes the results, discusses their efficiency, and demonstrates their relevance to privacy preserving computation of data mining algorithms, using generic constructions that can be applied to any function that has an efficient representation as a circuit. Expand
A classification based framework for privacy preserving data mining
TLDR
This paper demonstrates the difference between gini index and entropy attribute measures and proves that pruning results in accuracy and privacy. Expand
A Review on Privacy Preserving Data Mining using Secure Multiparty Computation
TLDR
This paper has considered a scenario where two different parties possesses confidential databases of their own and wish to run a data mining algorithm on the union of their databases, without disclosing any unnecessary information, and suggested different methodologies in order to preserve the privacy in the data mining process. Expand
Privacy preserving association rules mining on distributed homogenous databases
TLDR
This paper proposes a modification to privacy preserving association rule mining algorithm on distributed homogenous database that is faster, privacy preserving and provides accurate results. Expand
Privacy Preserving DBSCAN Algorithm for Clustering
TLDR
This paper proposed a protocols for how the distances are measured between data points, when the data is distributed across two parties, and proposed the first novel method for running DBSCAN algorithm operating over vertically and horizontally partitioned data sets, distributed in two different databases in a privacy preserving manner. Expand
Collusion-resistant privacy-preserving data mining
TLDR
This paper focuses its attention on the problem of collusions, in which some parties may collude and share their record to deduce the private information of other parties, and proposes a new method that entails a high level of security - full-privacy. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 61 REFERENCES
Privacy Preserving Data Mining
TLDR
This paper introduces the concept of privacy preserving data mining, and presents a solution that is considerably more efficient than generic solutions, and demonstrates that secure multi-party computation can be made practical, even for complex problems and large inputs. Expand
Limiting privacy breaches in privacy preserving data mining
TLDR
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. Expand
Privacy preserving mining of association rules
TLDR
A class of randomization operators are proposed that are much more effective than uniform randomization in limiting the breaches of privacy breaches and derived formulae for an unbiased support estimator and its variance are derived. Expand
Auditing Boolean attributes
TLDR
It is proved that the problem of auditing databases which support statistical sum queries to protect the security of sensitive information is NP-hard even in the two-dimensional case, and two efficient algorithms are proposed. Expand
Maintaining Data Privacy in Association Rule Mining
TLDR
This work presents a scheme, based on probabilistic distortion of user data, that can simultaneously provide a high degree of privacy to the user and retain a high level of accuracy in the mining results. Expand
Transforming data to satisfy privacy constraints
TLDR
This paper addresses the important issue of preserving the anonymity of the individuals or entities during the data dissemination process by the use of generalizations and suppressions on the potentially identifying portions of the data. Expand
Disclosure limitation of sensitive rules
Data products (macrodata or tabular data and micro-data or raw data records), are designed to inform public or business policy, and research or public information. Securing these products againstExpand
Protecting sensitive knowledge by data sanitization
TLDR
A new, efficient one-scan algorithm is introduced that meets privacy protection and accuracy in association rule mining, without putting at risk the effectiveness of the data mining per se. Expand
Practical Private Information Retrieval with Secure Coprocessors
TLDR
This paper abstracts the problem of how to implement a server that provides access to records in a large database in a way that ensures the complete privacy of this access (and, potentially, the contents of the records themselves)—even to the operator of this server—to a real world computer security application. Expand
Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
TLDR
The concept of minimal generalization is introduced, which captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and possible preference policies to choose among diierent minimal generalizations are illustrated. Expand
...
1
2
3
4
5
...