Full-support local randomizers can incur unnecessary reporting distortion on ordered alphabets. We study randomized response whose output is restricted to a radius-$t$ neighborhood of the private input. Input-dependent supports create privacy leakage at their boundaries, so ordinary likelihood-ratio calibration is no l...
A. Zamani, Parastoo Sadeghi, Mikael Skoglund· 0 citations
Information-theoretic generalization bounds usually control dependence on the task sample, whereas selective privacy may constrain only a different, private coordinate. We study i.i.d. pairs $(X,Y)$, where a learner observes $(X^n,Y^n)$, leaks at most $\epsilon_n$ nats about $X^n$, and incurs loss on $Y$. A chain-rule...
A. Zamani, Parastoo Sadeghi, Mikael Skoglund· 0 citations
We study the design of a statistical privacy mechanism under a bounded $\alpha$-lift leakage constraint. An agent observes useful data $Y$, which is arbitrarily correlated with private data $X$, and releases $U$. The goal is to maximize Pearson $\chi^2$-information $\chi^2(Y;U)$ while satisfying the point-wise privacy...
A. Zamani, Mohammad Amin Zarrabian, Parastoo Sadeghi et al.· 0 citations
A lossless data compression problem with a privacy constraint is studied where a server has access to a database of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> files, <inline-formula> <tex-math notation="LaTeX">$(Y_{1},\ldots,Y_{N})$ </tex-math></inline-formula>, each of size <inline-fo...
A. Zamani, Mikael Skoglund· IEEE Journal on Selected Are...· 0 citations
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