Nina Taft

Artificial Intelligence · 2002–2015

A researcher in the Science corpus: 11 papers; trusts 44 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #5,369 of 35,345 by trust here, #3,204 by citations
• Privacy & Security — 55 citations into it; #150 of 709 by trust here, #105 by citations
• Computer Networks and Communications — 50 citations into it; #3,855 of 9,886 by trust here, #774 by citations
• Computational Theory and Mathematics — 35 citations into it; #6,661 of 12,182 by trust here, #1,463 by citations
• Information Systems — 34 citations into it; #4,881 of 8,771 by trust here, #6,501 by citations
• Statistics and Probability — 17 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #1,751 of 2,251 by trust here, #827 by citations
• Computer Vision and Pattern Recognition — 14 citations into it

Whom Nina Taft trusts overall

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

1. Cynthia Dwork
2. Anukool Lakhina
3. Yehuda Lindell
4. Benny Pinkas
5. Frank McSherry
6. Yan Huang
7. Yehuda Koren
8. Vladimir Kolesnikov
9. Craig Gentry
10. Mark Crovella

Who trusts Nina Taft overall

1. Blaine Nelson
2. Yin Zhang
3. Battista Biggio
4. Adrià Gascón
5. Benjamin I. P. Rubinstein
6. Prateek Jain
7. Neil Zhenqiang Gong
8. Jinyuan Jia
9. Kamalika Chaudhuri
10. Abhradeep Thakurta

Papers from Nina Taft's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Fair exchange with a semi-trusted third party (extended abstract)
4. Protocols for secure computations
5. Calibrating Noise to Sensitivity in Private Data Analysis
6. Learning Multiple Layers of Features from Tiny Images
7. Gradient-based learning applied to document recognition
8. Differential Privacy
9. Fully homomorphic encryption using ideal lattices
10. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes

Their papers

• Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning (2009) — 301 citations
• ANTIDOTE: understanding and defending against poisoning of anomaly detectors (2009) — 428 citations
• Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013) — 490 citations
• GraphSC: Parallel Secure Computation Made Easy (2015) — 193 citations
• Privacy-preserving matrix factorization (2013) — 305 citations
• BlurMe: inferring and obfuscating user gender based on ratings (2012) — 194 citations
• Stealthy poisoning attacks on PCA-based anomaly detectors (2009) — 66 citations
• Compromising PCA-based Anomaly Detectors for Network-Wide Traffic (2008) — 33 citations
• Combining filtering and statistical methods for anomaly detection (2005) — 368 citations
• Structural analysis of network traffic flows (2004) — 598 citations
• Traffic matrix estimation: existing techniques and new directions (2002) — 581 citations