Shahab Asoodeh

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We investigate the tradeoff between privacy and utility in a situation where both privacy and utility are measured in terms of mutual information. For the binary case, we fully characterize this tradeoff in case of perfect privacy and also give an upper-bound for the case where some privacy leakage is allowed. We then introduce a new quantity which(More)
We consider the transmission of a single bit over the continuous-time Poisson channel with noiseless feedback. We show that to send the bit reliably requires, on the average, half the energy of a photon. In the absence of peak-power constraints this holds irrespective of the intensity of the dark current. We also solve for the energy required to send log 2(More)
A privacy-constrained information extraction problem is considered where for a pair of correlated discrete random variables (X,Y ) governed by a given joint distribution, an agent observes Y and wants to convey to a potentially public user as much information about Y as possible without compromising the amount of information revealed about X . To this end,(More)
We investigate the problem of the predictability of random variable Y under a privacy constraint dictated by random variable X, correlated with Y , where both predictability and privacy are assessed in terms of the minimum mean-squared error (MMSE). Given that X and Y are connected via a binary-input symmetric-output (BISO) channel, we derive the optimal(More)
The rate-privacy function is defined in [1] as a tradeoff between privacy and utility in a distributed private data system in which both privacy and utility are measured using mutual information. Here, we use maximal correlation in lieu of mutual information in the privacy constraint. We first obtain some general properties and bounds for maximal(More)
We investigate the problem of guessing a discrete random variable Y under a privacy constraint dictated by another correlated discrete random variable X, where both guessing efficiency and privacy are assessed in terms of the probability of correct guessing. We define h(P<inf>XY</inf>,&#x03B5;) as the maximum probability of correctly guessing Y given an(More)
Given a private source of information, Xn and a public correlated source, Yn, we study the problem of encoding the two-dimensional source (Xn; Yn) into an index J such that a remote party, knowing J and some external side information Zn, can losslessly recover Yn while any eavesdropper knowing J and possibly a correlated side information En can retrieve(More)
We investigate the problem of estimating a random variable Y ∈ Y under a privacy constraint dictated by another correlated random variable X ∈ X , where estimation efficiency and privacy are assessed in terms of two different loss functions. In the discrete case, we use the Hamming loss function and express the corresponding utility-privacy tradeoff in(More)
This paper proposes a new stopping criterion for turbo codes which is derived by observing the role of the unreliable bits in the received sequence. The proposed criterion computes the mean of inverse absolute LLR values and compares it with a pre-defined threshold to decide whether further iteration is needed or not. The simulations in different situations(More)