UCLA researchers in the Department of Computer Science have developed a new privacy preserving mechanism for stream analytics.
Computers and smartphones not only serve as a means of communication and computation, but also include a variety of sensors (e.g., proximity, accelerometer, gyroscopic, GPS, etc.). Various data analysts perform data aggregation studies and try to extract meaning from this sensor and user data. However, with this wide array of sensors comes serious privacy concerns with the large amount of real-time personal data that is collected without individual’s consent or knowledge. Of the more serious privacy violations is with regards to location data. While location based services are rising in popularity, the concerns of being constantly tracked require proportional privacy-preserving mechanisms. There needs to be a mechanism by which the data analysts should be able to receive the aggregated data in order to perform analysis but simultaneously preserve the user’s privacy at the same time.
Researcher Josh Joy and colleagues at UCLA have developed a novel privacy preserving tool for stream analytics that allows the user to control and privatize their data without the need for a ‘trusted’ third party. Additionally, this allows users to share their private data without actually being linked to the data, thereby preserving privacy. This allows the data analysts to make conclusions with aggregate data in real-time all while preserving end-user data privacy. This innovation streamlines data privacy management as the user privatizes the data, as opposed to a single data-owner, allowing for scalability of the service.
This technology has been validated with vehicle tracking data
|United States Of America
Cyber security, data encryption, encryption, privacy, data privacy, stream analytics, PAS-MC, location services, GPS tracking, location data, location tracking