With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive

Data anonymization , , , , , , data randomization , , , and cryptography , are some of the major techniques used in the field of privacy-preserving data mining or data publishing. k -anonymization is the process of anonymizing the records such that k individuals become indistinguishable from each other. Privacy preserving access control mechanism with accuracy Abstract -In recent years, isolation takes an imperative role to secure the data from various probable attackers. For public .While publishing collaborative data to multiple data provider’s two types of problem occurs, first is outsider attack and techniques with m-privacy techniques and addition of protocols as secure multiparty Search EUDL 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) in addition to the external background knowledge to infer the [PDF] Parallelizing K-Anonymity Algorithm for Privacy Disclosure control has become inevitable as privacy is given paramount importance while publishing data for mining. The data mining community enjoyed revival after Samarti and Sweeney proposed k-anonymization for privacy preserving data mining. The k-anonymity has gained high popularity in research circles. Though it has some drawbacks and other PPDM algorithms such as l-diversity, t-closeness

Vedangi A, Anandam V. Data slicing technique to privacy preserving and data publishing. IJRET: International Journal of Research in Engineering and Technology; Volume 02 Issue 10: pp.120-126. Google Scholar; Sathish. R SSK, Silambarashi G. A new approach for collaborative data publishing using slicing and m-privacy.

Privacy-Preserving Sequential Data Publishing | SpringerLink Dec 15, 2019

m-Privacy for Collaborative Data Publishing

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