Differentially private real-time data release based on the moving average strategy

• Main Authors: Daiyong Quan, Lu Yu, Mengang Li
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2017-01-01
• Series: Tehnički Vjesnik
• Subjects: differential privacy; dynamic allocation; context privacy protection; moving average approximate strategy
• Online Access: https://hrcak.srce.hr/file/273538

With the development and popularization of mobile-aware service systems, it is easy to collect contextual data such as activity trajectories in daily life. Releasing real-time statistics over context streams produced by crowds of people is expected to be valuable for both academia and business. However, analysing these raw data will entail risks of compromising individual privacy. ε-Differential Privacy has emerged as a standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. In the mobile-aware service systems, the ultimate goal is not only to protect the user's privacy, but look for a good balance between privacy and utility. To this end, we propose a flexible m-context privacy model to ensure user privacy under protection of ε-differential privacy. Experiments using two real-life datasets show that our proposed dynamic allocation of the privacy budget with moving average approximate strategy can work efficiently to release privacy preserved data in real-time.