Learning Light-Weight Edge-Deployable Privacy Models

Abstract Nowadays privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework implementation with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications. Our results also show that the anonymization process using pre-learned models significantly reduces time to obfuscate user data.
  • Yeon-Sup Lim (IBM US)
  • Mudhakar Srivatsa (IBM US)
  • Supriyo Chakraborty (IBM US)
  • Ian Taylor (Cardiff)
Date Dec-2018
Venue IEEE Big Data 2018