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Past Member

Praneeth Vepakomma

Former Research Assistant
Contact Information:
Groups
Project Research

Posthoc Privacy guarantees for neural network queries

Cloud based machine learning inference is an emerging paradigm where users query with their data to a service provider who runs a ML model …
Project Research

Computational Privacy for Computer Vision

Computer Vision applications are making a remarkable impact on society and advancing progress in Machine Learning. Our project aims to desi…
Project Research

Synthesizing data privately for Machine Learning

We propose sanitizer, a framework for secure and task-agnostic data release. While releasing datasets continues to make a big impact in var…
Project Research

PrivateMail: Supervised Manifold Learning of Deep Activations With Privacy

Differential Privacy offers strong guarantees such as immutable privacy under any post-processing. In this work, we propose a differentiall…
Project Research

QCSF: Parallel Combinatorial Optimization without needing Submodularity

Classes of set functions along with a choice of ground set are a bedrock to determine and develop corresponding variants of greedy algorith…
Project Research

Private measurement of nonlinear correlations between multiple parties

We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provid…
Project Research

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multip…
Project Research

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer…
Project Research

FedML: A Research Library and Benchmark for Federated Machine Learning

In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new 'federated learning algori…
Project Research

NoPeek

NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training (Won FG-2021 Mukh Best Paper Runner…
Project Research

DAMS: Differentially Private Sketching Data Structures

We propose an improved private count-mean-sketch data structure and show its applicability to differentially private contact trac…
Project Research

Splintering with distributions: A stochastic decoy scheme for private computation

We present a stochastic scheme for splitting the client data into privatized shares that are transmitted to the server in such settings. Th…
Project Research

Assessing Disease Exposure Risk With Location Histories And Protecting Privacy

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, n…
Project Research

Split Learning: Distributed and collaborative learning

Split Learning: Distributed deep learning without sharing raw data Project Page: https://splitlearning.github.io/Abstract: C…