Private Similarity Computation in Distributed Systems: From Cryptography to Differential Privacy
By Mohammad Alaggan, Sébastien Gambs, Anne-Marie Kermarrec. 2011
19 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Gilles Barthe
- Aurélien Bellet
- Santiago Zanella-B'eguelin
- Benjamin Gr'egoire
- César Kunz
- George Danezis
- Marc Tommasi
- Rachid Guerraoui
In Statistics and Probability
- Aurélien Bellet
- Santiago Zanella-B'eguelin
- Marc Tommasi
- Gilles Barthe
- George Danezis
- Rachid Guerraoui
- Benjamin Gr'egoire
- César Kunz
Citations in the corpus
- Personalized and Private Peer-to-Peer Machine Learning (2017, background): “) or directly from the local datasets, and can be computed in a private way (see e.g., Goethals et al., 2004; Alaggan et al., 2011).”
- Verified Computational Differential Privacy with Applications to Smart Metering (2013, methodology): “Another challenge is to provide tool-support for reasoning about the utility of differentially private algorithms, which would require more advanced methods to derive bounds about the probability of events w.r.t. distributions generated by programs (e.g. Chebyshev’s inequality).”