We are happy that Maya Benarous’ paper, “Synthesis of Longitudinal Human Location Sequences: Balancing Utility and Privacy“, was recently accepted to the ACM Transactions on Knowledge Discovery from Data (TKDD). The paper, written with Maya’s two co-advisors, Eran Toch and Irad Ben Gal, looks at synthesizing long sequences of people’s whereabouts. People’s location data is continuously tracked from a multitude of devices and sensors, enabling the ongoing analysis of sensitive information that can re-identify individuals or reveal sensitive information.

Maya had analyzed the use of different synthetic data generation models for long location sequences, including long short-term memory networks (LSTMs), Markov Chains, and variable-order Markov models (VMMs). The paper analyzes different performance measures, such as data similarity and privacy, and introduces different measurements to quantify each of these measures.  Her experiments, based on the anonymous data of 300 thousand users, show that different models can be used with different data analysis applications, such as traffic prediction or lifestyle analysis.