all InfoSec news
Ranking Differential Privacy. (arXiv:2301.00841v1 [stat.ML])
cs.CR updates on arXiv.org arxiv.org
Rankings are widely collected in various real-life scenarios, leading to the
leakage of personal information such as users' preferences on videos or news.
To protect rankings, existing works mainly develop privacy protection on a
single ranking within a set of ranking or pairwise comparisons of a ranking
under the $\epsilon$-differential privacy. This paper proposes a novel notion
called $\epsilon$-ranking differential privacy for protecting ranks. We
establish the connection between the Mallows model (Mallows, 1957) and the
proposed $\epsilon$-ranking differential privacy. …
comparisons differential privacy information life personal personal information privacy protect protection single under videos