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

Abstract

Original scientific paper 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. Keywords: differential privacy; dynamic allocation; context privacy protection; moving average approximate strategyOslobađanje diferencijalno privatnih podataka u realnom vremenu zasnovano na pokretnoj prosječnoj strategiji Izvorni znanstveni članak S razvojem i popularizacijom mobilno-svjesnih (mobile-aware) uslužnih sustava lako je prikupiti kontekstualne podatke kao što su putanje aktivnosti u svakodnevnom životu. Očekuje se da će objavljivanje postojećih statističkih podataka o kontekstualnim strujanjima koje proizvode mase ljudi biti od važnosti i za znanstvenike i za poslovne ljude. Ipak, analiza tih neobrađenih podataka može dovesti do kompromitiranja individualne privatnosti. ε-Differential Privacy pojavila se kao standard za objavljivanje privatnih statističkih podataka zbog toga što garantira preciznost i matematičku dokazivost. Kod mobilno-svjesnih uslužnih sustava krajnji cilj je ne samo zaštita korisnikove privatnosti već i stvaranje balansa između privatnosti i korisnosti. Imajući to u vidu mi predlažemo fleksibilni m-kontekst model privatnosti u svrhu osiguranja privatnosti korisnika pod zaštitom ε-diferencijalne privatnosti. Eksperimenti s dva niza podataka iz stvarnog života pokazuju da predložena raspodjela privatnog budžeta primjenom pokretne prosječne aproksimativne strategije može biti efikasna kod objavljivanja privatnih podataka u realnom vremenu.

Random Forest Algorithm Based on Linear Privacy Budget Allocation

“…However, when there are too many iterations, the privacy budget tends to be zero, resulting in poor data availability. In the real-time location protection of users, Li et al (2017) proposed a privacy budget allocation strategy with adaptive adjustment according to the distribution of underlying data. However, when the total number of users is too large, the privacy budget allocated on each timestamp is too small, resulting in the addition of too much noise.…”