Privacy Preserving Data Mining
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Sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed.
“In particular, sensitive data disclosure is a long-standing challenge in protecting anonymity when data is published and/or analysed [6,111,195].”
From Fairness in machine learning: A survey (2024) · cites this paper as [195]
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Deep Learning with Differential Privacy
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Membership Inference Attacks Against Machine Learning Models
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Privacy-Preserving Gradient-Descent Methods
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- Authors
- Lindell, Y., & Pinkas, B.
- Affiliations
- Weizmann Institute of Science (IL)
- ORCID
- Lindell, Yehuda
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2000
- Pages
- 36-54
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1528076390
- Cited by
- 1,077
- References
- 19
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 21 · 2023: 21 · 2022: 16 · 2021: 42 · 2020: 47 · 2019: 50 · 2018: 55 · 2017: 44 · 2016: 46 · 2015: 52 · 2014: 68 · 2013: 58 · 2012: 62
- FWCI
- 8.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving data mining, secure multi-party computation, decision tree induction, ID3, cryptographic protocols, privacy-preserving machine learning, medical records privacy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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