Who Cited It

Calibrating Noise to Sensitivity in Private Data Analysis

2006 · Lecture notes in computer science · 7,210 citations · 16 from inside this corpus

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith

No abstract in the source record.

Calibrating Noise to Sensitivity in Private Data Analysis (2006)Calibrating Noise to Sensitiv…k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY (2002)k-ANONYMITY: A MODEL FOR PROT…Privacy-preserving data mining (2000)Privacy-preserving data miningMachine learning: Trends, perspectives, and prospects (2015)Machine learning: Trends, per…Deep Learning with Differential Privacy (2016)Deep Learning with Differenti…Advances and Open Problems in Federated Learning (2020)Advances and Open Problems in…Differential Privacy (2006)Differential PrivacyFederated Learning: Challenges, Methods, and Future Directions (2020)Federated Learning: Challenge…Membership Inference Attacks Against Machine Learning Models (2017)Membership Inference Attacks …The Algorithmic Foundations of Differential Privacy (2013)The Algorithmic Foundations o…Fairness through awareness (2012)Fairness through awarenessDifferential Privacy: A Survey of Results (2008)Differential Privacy: A Surve…Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …Robust De-anonymization of Large Sparse Datasets (2008)Robust De-anonymization of La…On the Opportunities and Risks of Foundation Models (2021)On the Opportunities and Risk…Our Data, Ourselves: Privacy Via Distributed Noise Generation (2006)Our Data, Ourselves: Privacy …Privacy-preserving data publishing (2010)Privacy-preserving data publi…Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A… (2019)Comprehensive Privacy Analysi…RAPPOR (2014)RAPPOR
18 of 18 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY20028,585
Privacy-preserving data mining20002,997

What cites it, inside the corpus

PaperYearCited
Machine learning: Trends, perspectives, and prospects20159,947
Deep Learning with Differential Privacy20166,220
Advances and Open Problems in Federated Learning20205,383
Differential Privacy20065,337
Federated Learning: Challenges, Methods, and Future Directions20204,992
Membership Inference Attacks Against Machine Learning Models20174,490
The Algorithmic Foundations of Differential Privacy20134,058
Fairness through awareness20123,476
Differential Privacy: A Survey of Results20083,392
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
Robust De-anonymization of Large Sparse Datasets20082,349
On the Opportunities and Risks of Foundation Models20212,265
Our Data, Ourselves: Privacy Via Distributed Noise Generation20061,826
Privacy-preserving data publishing20101,651
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…20191,624
RAPPOR20141,548

Links

DOI · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Cryptography and Data SecurityComputer Science
Internet Traffic Analysis and Secure E-votingComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports15 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:43+00:00.

sha256 5cad55ac5d4d41e1…