An Improved Variable -Sized Microaggregation Algorithm for Privacy Preservation (IV-MDAV)
Gajendra Singh Rawat, Bhogeshwar Borah · 2015
Micro aggregation is a technique used to protect privacy in databases and location-based services. We propose a new hybrid technique for multivariate micro aggregation. Our technique combines a heuristic yielding fixed-size groups and a genetic algorithm yielding variable-sized groups. Fixed-size heuristics are fast and able to deal with large data sets, but they sometimes are far from optimal in terms of the information loss inflicted. On the other hand, the genetic algorithm obtains very good results (i.e. optimal or near optimal), but it can only cope with very small datasets. Our technique leverages the advantages of both types of heuristics and avoids their shortcomings. First, it partitions the data set into a number of groups by using a fixed- size heuristic. Then, it optimizes the partitions by means of the genetic algorithm. As an outcome of this mixture of heuristics, we obtain a technique that improves the results of the fixed-size heuristic in large data sets.INTRODUCTION- Over the last twenty years, there has been an extensive growth in the amount of private data collected about individuals. This data comes from a number of sources including medical, financial, library, telephone, and shopping records. Such data can be integrated and analyzed digitally as it’s possible due to the rapid growth in database, networking, and computing technologies. On the one hand, this has led to the development of data mining tools that aim to infer useful trends from this data. But, on the other hand, easy access to personal data poses a threat to individual privacy. This has lead to concerns that the personal data may be misused for a variety of purposes. Detailed person-specific data in its original form often contains sensitive information about individuals, and publishing such data immediately violates individual privacy.