English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Decentralized optimization is increasingly popular in machine learning forits scalability and efficiency. Intuitively, it should also provide betterprivacy guarantees, as nodes only observe the messages sent by their neighborsin the network graph. But formalizing and quantifying this gain is challenging:existing results are typically limited to Local Differential Privacy (LDP)guarantees that overlook the advantages of decentralization. In this work, weintroduce pairwise network differential privacy, a relaxation of LDP thatcaptures the fact that the privacy leakage from a node $u$ to a node $v$ maydepend on their relative position in the graph. We then analyze the combinationof local noise injection with (simple or randomized) gossip averaging protocolson fixed and random communication graphs. We also derive a differentiallyprivate decentralized optimization algorithm that alternates between localgradient descent steps and gossip averaging. Our results show that ouralgorithms amplify privacy guarantees as a function of the distance betweennodes in the graph, matching the privacy-utility trade-off of the trustedcurator, up to factors that explicitly depend on the graph topology. Finally,we illustrate our privacy gains with experiments on synthetic and real-worlddatasets.

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging