Indirect Disclosures in Data Mining

Privacy preserving distributed mining algorithms mine distributed data while ensuring that one´s private contribution to the global computation is not revealed. However, there are instances when such privacy assurances may fail. For example, if one´s contribution happens to be an outlier, its data can be estimated from the globally mined data. In this paper we propose two simple protocols to address such indirect disclosure issues. Our work, though simple, is a bit novel: the first protocol establishes a direct relationship between a well known problem - dining cryptographers - and ours, while the second protocol extends an existing approach to computing global sum.