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Found 4 papers, 0 papers with code
Directional Privacy for Deep Learning
In this paper, we apply \textit{directional privacy}, via a mechanism based on the von Mises-Fisher (VMF) distribution, to perturb gradients in terms of \textit{angular distance} so that gradient direction is broadly preserved.
Locality Sensitive Hashing with Extended Differential Privacy
Then we show that our mechanisms enable friend matching with high utility and rigorous privacy guarantees based on extended DP.
Generalised Differential Privacy for Text Document Processing
We address the problem of how to "obfuscate" texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text.
Author Obfuscation Using Generalised Differential Privacy
The problem of obfuscating the authorship of a text document has received little attention in the literature to date.
Cryptography and Security
