Private Filtering for Hidden Markov Models

• 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. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Nyckelord

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

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)