Ada Wai-Chee Fu

Artificial Intelligence · 2002–2010

A researcher in the Science corpus: 6 papers; trusts 21 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #5,366 of 35,345 by trust here, #8,283 by citations
• Sociology and Political Science — 18 citations into it; #10,832 of 22,756 by trust here, #13,822 by citations
• Management Science and Operations Research — 9 citations into it
• Privacy & Security — 8 citations into it
• Signal Processing — 8 citations into it; #1,282 of 7,572 by trust here, #2,457 by citations

Whom Ada Wai-Chee Fu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Latanya Sweeney
2. Xiaokui Xiao
3. Pierangela Samarati
4. Yufei Tao
5. Ke Wang
6. Eamonn J. Keogh
7. Ashwin Machanavajjhala
8. Johannes Gehrke
9. Kristen LeFevre
10. Daniel Kifer

Who trusts Ada Wai-Chee Fu overall

1. Benjamin C. M. Fung
2. Rui Chen
3. Varun Chandola
4. Sebastian Schmidl
5. Tak-Chung Fu
6. Rohit J. Kate
7. Tiancheng Li
8. Mohsin Munir
9. Philip S. Yu
10. Noman Mohammed

Papers from Ada Wai-Chee Fu's perspective

Ordered by how strongly the researchers Ada Wai-Chee Fu trusts endorse each paper. Same corpus, a different order for every researcher.

1. k-Anonymity: A Model for Protecting Privacy
2. On the complexity of optimal K-anonymity (ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems)
3. Protecting Respondents' Identities in Microdata Release
4. Anatomy: simple and effective privacy preservation (Very Large Data Bases Conference)
5. Injecting utility into anonymized datasets (SIGMOD Conference)
6. Personalized privacy preservation (Privacy-Preserving Data Mining)
7. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
8. Bottom-up generalization: a data mining solution to privacy protection (Industrial Conference on Data Mining)
9. Top-down specialization for information and privacy preservation (IEEE International Conference on Data Engineering)
10. Worst-Case Background Knowledge for Privacy-Preserving Data Publishing (IEEE International Conference on Data Engineering)

Their papers

• HOT SAX: efficiently finding the most unusual time series subsequence (2005) — 850 citations
• Utility-based anonymization using local recoding (2006) — 485 citations
• K-isomorphism: privacy preserving network publication against structural attacks (2010) — 391 citations
• Minimality Attack in Privacy Preserving Data Publishing (2007) — 327 citations
• (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing (2006) — 728 citations
• Enhancing Effectiveness of Outlier Detections for Low Density Patterns (2002) — 662 citations