Semantic Search Log k-Anonymization with Generalized k-Cores of Query Concept Graph

Search log k-anonymization is based on the elimination of infrequent queries under exact (or nearly exact) matching conditions, which usually results in a big data loss and impaired utility. We present a more flexible, semantic approach to k-anonymity that consists of three steps: query concept mining, automatic query expansion, and affinity assessment of expanded queries. Based on the observation that many infrequent queries can be seen as refinements of a more general frequent query, we first model query concepts as probabilistically weighted ngrams and extract them from the search log data. Then, after expanding the original log queries with their weighted concepts, we find all the k-affine expanded queries under a given affinity threshold Θ, modeled as a generalized k-core of the graph of Θ-affine queries. Experimenting with the AOL data set, we show that this approach achieves levels of privacy comparable to those of plain k-anonymity while at the same time reducing the data losses to a great extent.