Anonymizing Unstructured Data

TL;DR

This paper introduces a formal framework for anonymizing set-valued data, such as search logs, by extending k-anonymity, and provides efficient algorithms with practical validation on real datasets.

Contribution

It formalizes k-anonymity for set-valued data and offers approximation algorithms with proven bounds, addressing privacy in unstructured datasets.

Findings

Algorithms achieve O(klogk) and O(1)-approximation guarantees.

Applicable to real-world datasets like AOL query logs.

Demonstrates effectiveness of anonymization methods.

Abstract

In this paper we consider the problem of anonymizing datasets in which each individual is associated with a set of items that constitute private information about the individual. Illustrative datasets include market-basket datasets and search engine query logs. We formalize the notion of k-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. We define an optimization problem that arises from this definition of anonymity and provide O(klogk) and O(1)-approximation algorithms for the same. We demonstrate applicability of our algorithms to the America Online query log dataset.