Third, we present a data provider-aware anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiency. Finally, we propose secure multi-party computation protocols for collaborative data publishing with m -privacy.
Practical --Anonymization for Collaborative Data Abstract. In collaborative data publishing (CDP), an -adversary attack refers to a scenario where up to malicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and … Secure Distributed Data Anonymization and Integration with algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiency. Finally, we propose secure multi-party computation protocols for collaborative data publishing with m-privacy. All protocols are extensively analyzed, and their security and efficiency are formally proved. Privacy-Preserving Sequential Data Publishing | SpringerLink Dec 15, 2019
The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf
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C-mixture and multi-constraints based genetic algorithm
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract — This paper mainly deals with the issue of privacy preserving in data mining while collaborating n number of parties and trying to maintain confidentiality of all data providers details while collaborating their database. Here two type of attacks are addressed “insider attack ” and “outsider attack”. M-Partition Privacy Scheme to Anonymizing Set-Valued Data Abstract: In distributed databases there is an increasing need for sharing data that contain personal information. The existing system presented collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. M-privacy guarantees that anonymized data satisfies a given privacy constraint against any