Skip to main content
Main Menu
Utility Menu
Search
HARVARD.EDU
Harvard University Privacy Tools Project
Contact
Open Positions
Search
Home
Research
OpenDP: An Open-Source Suite of Differential Privacy Tools
Towards an End-to-End Approach to Formal Privacy for Sample Surveys
Privacy Tools for Sharing Research Data
Computing Over Distributed Sensitive Data
Applying Theoretical Advances in Privacy
Formal Privacy Models and Title 13
DataTags Research
Differential Privacy
Bridging Privacy Definitions
Sotto Voce Differentially Private Federated Learning Speech Recognition
News
People
Senior Personnel
Senior Researchers
Collaborators & Visitors
Junior Personnel
Postdocs & Fellows
Graduate Students
Undergraduate Students
Law Interns
Staff
IQSS Dataverse team
Publications
Software
OpenDP
DataTags.org
PSI (Differential Privacy Tool)
AbcDatalog
Outreach
Training Students & Researchers
Courses & Educational Materials
Public Events & Writings
Policy Engagement
Symposia & Workshops Organized
Open Seminars
HOME
/
PUBLICATIONS
/
Publications by Year: 2000
BibTex
Tagged
XML
2000
Latanya Sweeney
. 2000. “
Simple Demographics Often Identify People Uniquely
.” Carnegie Mellon University, Data Privacy.
Project website
PDF
Publications by Grant
Towards an End-to-End Approach to Formal Privacy for Sample Surveys - Publication
Computing over Distributed Sensitive Data: Publications
Privacy Tools for Sharing Research Data: Publications
Applying Theoretical Advances in Privacy to Computational Social Science Practice: Publications
Formal Privacy Models and Title 13: Publications
Publications by Year
2022
(10)
2021
(10)
2020
(13)
2019
(11)
2018
(16)
2017
(19)
2016
(27)
2015
(30)
2014
(20)
2013
(16)
1 of 2
»
Recent Publications
Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling.
Private rank aggregation in central and local models.
Private sequential hypothesis testing for statisticians: Privacy, error rates, and sample size.
Differentially Private Simple Linear Regression
Multiclass versus Binary Differentially Private PAC Learning
Legal Theorems: Bridging Computer Science and Privacy Law
«
2 of 31
»
More
Share