Seeking to address the -adversary problem, Goryczka et al. [] introduce the concept of -privacy which is the focus of this paper. More specifically, we focus on -privacy with respect to -anonymity, which is referred to as --anonymity in the remainder of this paper.Suppose that the total number of providers participating in the collaborative data publishing is , and the published data after
Solutions seeking to address such a scenario are known as collaborative privacy-preserving data publishing (CPPDP). CPPDP has received considerable attention in recent years (e.g., [4–11]). A straightforward solution is for all providers to outsource their data to a TTP, who will assume control of the data as if the TTP is publishing its own with adaptive m-privacy checking strategies . This will provide us high utility and m-privacy of anonymized data with higher efficiency. Finally, we are going to propose secure multi-party computation protocols (SMC) for collaborative data publishing with m-privacy. Here we can use either a In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of “insider attack” by colluding data providers who may use their own data records (a subset of the overall data) to infer the data records contributed by other data providers. ensure that the anonymized data fulfils a given protection requrements against any group of up to m colluding data providers. Two algorithms for collaborative data are: one is a heuristic algorithm which is checking m-privacy anonymized data from the provider and second algorithm is provider aware anonymization which ensures m-privacy Dec 15, 2019 · Machine learning in artificial intelligence relies on legitimate big data, where the process of data publishing involves a large number of privacy issues. m -Invariance is a fundamental privacy-preserving notion in microdata republication.
Privacy Characterization and Quantification in Data Publishing
IEEE Transactions on Knowledge and Data Engineering (TKDE), Special Issue on Peer-to-Peer Based Data Management, 16(7), July, 2004 L. Xiong, L. Liu. A Reputation-Based Trust Model for Peer-to-Peer eCommerce Communities . Academic Libraries and Research Data Services
Secure Multi Party Computation Protocols for Collaborative
The collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers a new type of “insider attack” by colluding data providers who may use their own data … Benjamin C. M. Fung - Publications - Data Mining m-privacy for collaborative data publishing. IEEE Transactions on Knowledge and Data Engineering ( TKDE ) , 26(10):2520-2533, October 2014. IEEE Computer Society.