Efficient Time-Stamped Event Sequence Anonymization
With the rapid growth of applications which generate timestamped sequences (click streams, GPS trajectories, RFID sequences), sequence anonymization has become an important problem, in that should such data be published or shared. Existing trajectory anonymization techniques disregard the importance of time or the sensitivity of events. This article is the first, to our knowledge, thorough study on time-stamped event sequence anonymization. We propose a novel and tunable generalization framework tailored to event sequences. We generalize time stamps using time intervals and events using a taxonomy which models the domain semantics. We consider two scenarios: (i) sharing the data with a single receiver (the SSR setting), where the receiver's background knowledge is confined to a set of time stamps and time generalization suffices, and (ii) sharing the data with colluding receivers (the SCR setting), where time generalization should be combined with event generalization. For both cases, we propose appropriate anonymization methods that prevent both user identification and event prediction. To achieve computational efficiency and scalability, we propose optimization techniques for both cases using a utility-based index, compact summaries, fast to compute bounds for utility, and a novel taxonomy-aware distance function. Extensive experiments confirm the effectiveness of our approach compared with state of the art, in terms of information loss, range query distortion, and preserving temporal causality patterns. Furthermore, our experiments demonstrate efficiency and scalability on large-scale real and synthetic datasets.
anonymity design algorithms performance experimentation privacy-preserving data publishing user browsing history time and url generalization