POkA

Abstract

Data generalization is widely used to protect identities and prevent inference of sensitive information during the public release of microdata. The k-anonymity model has been extensively applied in this context. The model seeks a generalization scheme such that every individual becomes indistinguishable from at least k-1 other individuals and the loss in information while doing so is kept at a minimum. The search is performed on a domain hierarchy lattice where every node is a vector signifying the level of generalization for each attribute. An effort to understand privacy and data utility trade-offs will require knowing the minimum possible information losses of every possible value of k. However, this can easily lead to an exhaustive evaluation of all nodes in the hierarchy lattice. In this paper, we propose using the concept of Pareto-optimality to obtain the desired trade-off information. A Pareto-optimal generalization is one in which no other generalization can provide a higher value of k without increasing the information loss. We introduce the Pareto-Optimal k-Anonymization (POkA) algorithm to traverse the hierarchy lattice and show that the number of node evaluations required to find the Pareto-optimal generalizations can be significantly reduced. Results on a benchmark data set show that the algorithm is capable of identifying all Pareto-optimal nodes by evaluating only 20% of nodes in the lattice.