Phyllis Reuther

Regularity-based wireless subscriber population estimation

Fine-grained dynamic population estimation is in an increasingly high demand as it has numerous applications in wireless network engineering, urban planning, location-based services and mobile applications, and advertisement. In this paper, we introduce a framework that dynamically estimates the wireless subscriber population of an arbitrary fine-grained area based on the current cellular phone usage. This framework takes advantage of strong regularities, low variance, and low information entropy in human mobility and phone usage patterns; thus simplifying the estimation for wireless carriers and other big entities while maintaining a high accuracy. We implemented our ‘regularity-based’ framework using empirical data. Comparison with experimentally collected data shows a significant improvement in the accuracy of population estimation compared to population count based on cellular phone usage.

Fingerprinting Data Based on Secret Mask

The value of massive data, e.g., customer service logs, is growing. Customer service log records are used to analyze market trends and diagnose service outage or performance degradation. Doing so, the data owner shares the log records with trusted third parties. The party, however, may violate the contract and illegally share with other non-trusted parties. To address this misbehavior, we propose a mask-based watermarking on the customer telecommunication log records. The watermark is added to the original data and later used to discover which party the owner shares the data. We analyze the utility of the watermarked data after applying our algorithm and also demonstrate that our algorithms are resilient against various attacks via risk analysis.