Cloud based machine learning inference is an emerging paradigm where users query with their data to a service provider who runs a ML model …
Computer Vision applications are making a remarkable impact on society and advancing progress in Machine Learning. Our project aims to desi…
We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in var…
Differential Privacy offers strong guarantees such as immutable privacy under any post-processing. In this work, we propose a differentiall…
Classes of set functions along with a choice of ground set are a bedrock to determine and develop corresponding variants of greedy algorith…
We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provid…
Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multip…
Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer…
In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new 'federated learning algori…
NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training (Won FG-2021 Mukh Best Paper Runner…
We propose an improved private count-mean-sketch data structure and show its applicability to differentially private contact trac…
We present a stochastic scheme for splitting the client data into privatized shares that are transmitted to the server in such settings. Th…
Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, n…
Split Learning: Distributed deep learning without sharing raw data Project Page: https://splitlearning.github.io/Abstract: C…