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Private Filtering for Hidden Markov Models
RISE - Research Institutes of Sweden, ICT, Acreo.
KTH Royal Institute of Technology, Sweden.
2018 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361Article in journal (Refereed) In press
Abstract [en]

Consider a hidden Markov model describing a system with two types of states: a monitored state and a private state. The two types of states are dependent and evolve jointly according to a Markov process with a stationary transition probability. It is desired to reveal the monitored states to a receiver but hide the private states. For this purpose, a privacy filter is necessary which suitably perturbs the monitored states before communication to the receiver. Our objective is to design the privacy filter to optimize the trade-off between monitoring accuracy and privacy, measured through a time-invariant distortion measure and Shannon's equivocation, respectively. As the optimal privacy filter is difficult to compute using dynamic programming, we adopt a suboptimal greedy approach through which the privacy filter can be computed efficiently. Here, the greedy approach has the additional advantage of not being restricted to finite time horizon setups. Simulations show the superiority of the approach compared to a privacy filter which only adds independent noise to the observations. 

Place, publisher, year, edition, pages
2018.
Keyword [en]
Dynamic programming, Greedy algorithm, Hidden Markov models, Privacy, Data privacy, Economic and social effects, Trellis codes, Distortion measures, Finite time horizon, Greedy algorithms, Greedy approaches, Independent noise, Monitoring accuracy, Shannon's equivocation, Transition probabilities
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33760DOI: 10.1109/LSP.2018.2827878Scopus ID: 2-s2.0-85045612606OAI: oai:DiVA.org:ri-33760DiVA, id: diva2:1204198
Available from: 2018-05-07 Created: 2018-05-07 Last updated: 2018-05-14Bibliographically approved

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