Efficient Anonymization Algorithms to Prevent Generalized Losses and Membership Disclosure in Microdata

Abstract

Nowadays, data and knowledge extracted by data mining techniques represent a key asset driving research, innovation, and policy-making activities. Many agencies and organizations have recognized the need of accelerating such trends and are therefore willing to release the data they collected to other parties, for purposes such as research and the formulation of public policies. However, the data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result privacy breaches, this is the case for the examples of micro-data, e.g., census data and medical data. This thesis studies how we can publish and share micro data in privacy-preserving manner. This present a next ensive study of this problem along three dimensions: Designing a simple, intuitive, and robust privacy model, designing an effective anonymization technique that works on sparse and high-dimensional data and developing a methodology for evaluating privacy and utility tradeoffs. Here, we present a novel technique called slicing which partitions the data both horizontally and vertically. It preserves better data utility than generalization and is more effective than bucketization in terms of sensitive attribute.