A General Framework for Privacy-Preserving Distributed Greedy Algorithm
This work addresses privacy concerns for data providers in distributed online applications, though it appears incremental as it builds upon existing greedy algorithms.
The paper tackles the problem of privacy in distributed greedy algorithms by proposing a framework that converts existing algorithms into privacy-preserving versions, achieving the same results while protecting private data from different parties.
Increasingly more attention is paid to the privacy in online applications due to the widespread data collection for various analysis purposes. Sensitive information might be mined from the raw data during the analysis, and this led to a great privacy concern among people (data providers) these days. To deal with this privacy concerns, multitudes of privacy-preserving computation schemes are proposed to address various computation problems, and we have found many of them fall into a class of problems which can be solved by greedy algorithms. In this paper, we propose a framework for distributed greedy algorithms in which instances in the feasible set come from different parties. By our framework, most generic distributed greedy algorithms can be converted to a privacy preserving one which achieves the same result as the original greedy algorithm while the private information associated with the instances is still protected.