m-Privacy Preserving Data Analysis And Data Publising
Keywords:
m-Privacy, k-anonymity, l-diversity, Database Management, Heuristic algorithms, Distributed Data Publising, Pruning StrategiesAbstract
Combining and analyzing data collected at multiple administrative locations is critical for a wide variety of applications, such as detecting malicious attacks or computing an accurate estimate of the popularity of Web sites. However, legitimate concerns about privacy often inhibit participation in collaborative data analysis. In this paper, we design, implement, and evaluate a practical solution for privacy-preserving data analysis and data publishing among a large number of participants. There is an increasing need for sharing data that contain personal information from distributed databases. For example, in the healthcare domain, a national agenda is to develop the Nationwide Health Information Network (NHIN) to share information among hospitals and other providers, and support appropriate use of health information beyond direct patient care with privacy protection. Privacy preserving data analysis and data publishing has received considerable attention in recent years as promising approaches for sharing data while preserving individual privacy. When the data are distributed among multiple data providers or data owners, two main settings are used for anonymization. One approach is for each provider to anonymize the data independently (anonymize-and-aggregate), which results in potential loss of integrated data utility.
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