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Found 4 papers, 1 papers with code
Information Leakage in Embedding Models
We demonstrate that embeddings, in addition to encoding generic semantics, often also present a vector that leaks sensitive information about the input data.
That which we call private
Instead, the definitions so named are the basis of refinements and more advanced analyses of the worst-case implications of attackers---without any change assumed in attackers' powers.
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value.
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