Privacy Against Agnostic Inference Attacks in Vertical Federated Learning

no code implementations • 10 Feb 2023 • Morteza Varasteh

The level of the distortion imposed on the passive party's parameters is adjustable, giving rise to a trade-off between privacy of the passive party and interpretabiliy of the VFL outcomes by the active party.

Inference Attack Privacy Preserving +1

Privacy Against Inference Attacks in Vertical Federated Learning

no code implementations • 24 Jul 2022 • Borzoo Rassouli, Morteza Varasteh, Deniz Gunduz

In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i. e., the active party, can employ to reconstruct the passive party's features, regarded as sensitive information.

Inference Attack Privacy Preserving +1

Learning to Communicate and Energize: Modulation, Coding and Multiple Access Designs for Wireless Information-Power Transmission

no code implementations • 14 Sep 2019 • Morteza Varasteh, Jakob Hoydis, Bruno Clerckx

Relying on the proposed model, the learning problem of modulation design for Simultaneous Wireless Information-Power Transmission (SWIPT) over a point-to-point link is studied.

Information Theory Signal Processing Information Theory