Rebecca N. Wright

Artificial Intelligence · 2001–2018

A researcher in the Science corpus: 9 papers; trusts 36 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #7,063 of 35,345 by trust here, #6,720 by citations
• Privacy & Security — 58 citations into it; #468 of 709 by trust here, #434 by citations
• Computational Theory and Mathematics — 38 citations into it; #10,662 of 12,182 by trust here, #2,629 by citations
• Computer Networks and Communications — 18 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Management Science and Operations Research — 13 citations into it
• Sociology and Political Science — 12 citations into it
• Statistics and Probability — 12 citations into it

Whom Rebecca N. Wright trusts overall

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

1. Cynthia Dwork
2. Oded Goldreich
3. Jaideep Vaidya
4. Frank McSherry
5. Kobbi Nissim
6. Irit Dinur
7. Latanya Sweeney
8. Stanley R. M. Oliveira
9. Alexandre V. Evfimievski
10. Bart Goethals

Who trusts Rebecca N. Wright overall

1. Reza Shokri
2. Bo Liu
3. Jinfei Liu
4. Claudio Orlandi
5. Andreas Holzer
6. Nguyen Binh Truong
7. Vitaly Shmatikov
8. Lingchen Zhao
9. Daniel Kifer
10. Zhongshu Gu

Papers from Rebecca N. Wright's perspective

Ordered by how strongly the researchers Rebecca N. Wright trusts endorse each paper. Same corpus, a different order for every researcher.

1. On the design and quantification of privacy preserving data mining algorithms
2. Calibrating Noise to Sensitivity in Private Data Analysis
3. Privacy Preserving Data Mining
4. Security-control methods for statistical databases: a comparative study
5. Revealing information while preserving privacy
6. ImageNet classification with deep convolutional neural networks
7. Limiting privacy breaches in privacy preserving data mining
8. On Private Scalar Product Computation for Privacy-Preserving Data Mining
9. Deep Learning with Differential Privacy
10. Boosting and Differential Privacy

Their papers

• Privacy-preserving Machine Learning as a Service (2018) — 275 citations
• Secure multiparty computation of approximations (2001) — 208 citations
• From Keys to Databases - Real-World Applications of Secure Multi-Party Computation (2018) — 154 citations
• A Practical Differentially Private Random Decision Tree Classifier (2009) — 221 citations
• Pan-private algorithms via statistics on sketches (2011) — 99 citations
• DP-WHERE: Differentially private modeling of human mobility (2013) — 147 citations
• Privacy-Oriented Cryptography (Dagstuhl Seminar 12381) (2012)
• Anonymity-preserving data collection (2005) — 111 citations
• Privacy-preserving distributed k-means clustering over arbitrarily partitioned data (2005) — 476 citations