David Zeber

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #34,716 of 35,345 by trust here, #20,836 by citations
• Privacy & Security — 45 citations into it; #206 of 709 by trust here, #192 by citations
• Computer Science Applications — 9 citations into it
• Algorithms & Complexity — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom David Zeber trusts overall

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

1. Cynthia Dwork
2. Frank McSherry
3. Ú. Erlingsson
4. Aleksandra Korolova
5. Michael Kearns
6. Kobbi Nissim
7. Adam D. Smith
8. Shiva Prasad Kasiviswanathan

Who trusts David Zeber overall

1. Badih Ghazi
2. Borja Balle
3. Albert Cheu
4. James Bell
5. Vitaly Feldman
6. Yatharth Dubey

Papers from David Zeber's perspective

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

1. Calibrating Noise to Sensitivity in Private Data Analysis
2. Randomized response: a survey technique for eliminating evasive answer bias.
3. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
4. A firm foundation for private data analysis
5. What Can We Learn Privately?
6. The Algorithmic Foundations of Differential Privacy
7. Our Data, Ourselves: Privacy Via Distributed Noise Generation
8. Local Privacy and Statistical Minimax Rates
9. Limiting privacy breaches in privacy preserving data mining
10. Pseudorandom Bits for Polynomials

Their papers

• BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017) — 102 citations
• Distributed Differential Privacy via Shuffling (2018) — 433 citations
• Distributed Differential Privacy via Mixnets (2018) — 21 citations