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  •  About
    • About the Lab
    • Director’s Note
    • Our Vision
    • Founding Donor
    • Advisory Board
    • Principal Investigators
  • People
    • Associated Faculty
    • Executive Committee
    • Students
    • Program Directorate
  • TrustNet
  •  Projects
  •  Resources
    • Pre-Doctoral Program
    • Internships
    • Early Career Award
    • Trust Lab Grant
    • Trust Lab Fellowship
  •  News
    • Trust Matters
    • Quick Updates
  •  Events
    • Talks
    • Trust Summit
    • TL CTF
    • Schools
    • All Events
  •  Engage

Differential Privacy

Overview
People
Outcome
Overview

Differential Privacy (DP) provides a mathematical framework to ensure privacy for individuals when their data is collected into large databases and used in various applications. In this project, we study novel extensions and aspects of DP.In one line of work, motivated by recommendation and other services in online social networks, we are trying to develop differentially private algorithms on graphs, which would still provide high accuracy/user satisfaction. In another line of work, we broaden the scope of differential privacy by developing alternate notions of accuracy and privacy.
Active from 2021

People

Soumen Chakrabarti

Abir De

Manoj Prabhakaran

Outcome
  • Aman Bansal, Rahul Chunduru, Deepesh Data and Manoj Prabhakaran, “Flexible Accuracy for Differential Privacy,” AISTATS 2022.
  • Abir De and Soumen Chakrabarti, “Differentially Private Link Prediction with Protected Connections,” AAAI 2021.
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    Department of Computer Science and Engineering,
    Indian Institute of Technology Bombay,
    Powai, Mumbai 400076
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