Damle, Sankarshan
Triastcyn, Aleksei
Faltings, Boi
Gujar, Sujit
Article History
Accepted: 18 January 2024
First Online: 2 March 2024
Declarations
:
: Authors have no competing interests as defined by Springer.
: Some text passages of this manuscript (e.g., preliminaries) have been drawn from our prior work published as a conference paper [CitationRef removed]. The current manuscript differs from the conference paper as follows: We provide a concrete example to highlight the privacy leak in SD-Gibbs (the state-of-the-art DCOP algorithm). We introduce a novel privacy metric, namely solution privacy, to study the additional information leak in privacy-preserving DCOP algorithms. In [CitationRef removed], we only provide proof sketches. The current manuscript provides formal proofs for each result presented. We provide an additional set of experiments, including (i) an additional benchmark and (ii) concerning P-Gibbs’ hyperparameters to study the specific impact of each hyperparameter on the quality of P-Gibbs’ solution and the privacy budget. We introduce a novel metric, namely assignment distance, to explain the privacy protection in P-Gibbs compared to SD-Gibbs.