Differential Privacy Mechanisms for DAP
draft-wang-ppm-differential-privacy-00
Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
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|
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Authors | Junye Chen , Audra McMillan , Christopher Patton , Kunal Talwar , Shan Wang | ||
Last updated | 2024-04-25 (Latest revision 2023-10-23) | ||
RFC stream | (None) | ||
Intended RFC status | (None) | ||
Formats | |||
Stream | Stream state | (No stream defined) | |
Consensus boilerplate | Unknown | ||
RFC Editor Note | (None) | ||
IESG | IESG state | Expired | |
Telechat date | (None) | ||
Responsible AD | (None) | ||
Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
Differential Privacy (DP) is a property of a secure aggregation mechanism that ensures that no single input measurement significantly impacts the distribution of the aggregate result. This is a stronger property than what systems like the Distributed Aggregation Protocol (DAP) are designed to achieve. This document describes a variety of DP mechansisms applicable to DAP, and, for a variety of common use cases, lifts DAP to a protocol that also achieves DP.
Authors
Junye Chen
Audra McMillan
Christopher Patton
Kunal Talwar
Shan Wang
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)