Monitoring Web Browsing Behavior with Differential Privacy

Monitoring web browsing behavior has benefited many data mining applications, such as top-K discovery and anomaly detection. However, releasing private user data to the greater public would concern web users about their privacy, especially after the incident of AOL search log release where anonymization was not correctly done. In this paper, we adopt differential privacy, a strong, provable privacy definition, and show that differentially private aggregates of web browsing activities can be released in real-time while preserving the utility of shared data. Our proposed algorithms utilize the rich correlation of the time series of aggregated data and adopt a state-space approach to estimate the underlying, true aggregates from the perturbed values by the differential privacy mechanism. We evaluate our algorithms with real-world web browsing data. Utility evaluations with three metrics demonstrate that the quality of the private, released data by our solutions closely resembles that of the original, unperturbed aggregates.