Privacy-Concerned Parallel Distributed Bayesian Sequential Detection
2014 (English)In: Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) 2014, IEEE Signal Processing Society, 2014, 928-932 p.Conference paper (Refereed)
In this paper, eavesdropping in parallel distributed sequential detections is considered. The privacy risk is evaluated by the minimal achievable Bayesian risk of a greedy and informed eavesdropper who is curious about the hypothesis realization. We propose a novel metric based on Bayesian risk to take the detection performance and privacy risk with different weights into account. We formulate and study the privacy-concerned parallel distributed Bayesian sequential detection problem under a finite time-horizon assumption. Solving this problem will lead to the optimal distributed sequential detection design which achieves the minimal privacy-concerned Bayesian risk. The study shows that it is not sufficient to consider a deterministic likelihood-ratio test for a remote decision maker at an active time index in the optimal privacy-concerned system design. However, properties of the optimal design indicate that the standard method can be extended to solve the proposed problem.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2014. 928-932 p.
Dynamic programming; Eavesdropper; Person-by-person optimization; Physical-layer secrecy
Communication Systems Signal Processing
IdentifiersURN: urn:nbn:se:kth:diva-151262DOI: 10.1109/GlobalSIP.2014.7032256ScopusID: 2-s2.0-84949927707OAI: oai:DiVA.org:kth-151262DiVA: diva2:747372
IEEE Global Conference on Signal and Information Processing (GlobalSIP) 2014, Atlanta, USA, Dec. 3-5, 2014
QC 201602092014-09-162014-09-162016-02-09Bibliographically approved