Data security and null value imputation in distributed information systems

Distributed Information System (DIS) is seen as a collection of autonomous information systems which can collaborate with each other. This collaboration can be driven by requests for knowledge needed to predict what values should replace null values in missing or incomplete attributes. Any incompleteness in data can be seen either as the result of a partial knowledge about properties of objects stored in DIS or some attributes might be just hidden from users because of the security reason. Clearly, in the second case, we have to be certain that the missing values can not be predicted from the available data by chase, distributed chase or any other null value imputation method. Let us assume that an attributes d is hidden at one of the sites of DIS, denoted by S and called a client. With a goal to reconstruct this hidden attribute, a request for a definition of this attribute can be sent by S to some of its remote sites (see [15]). These definitions stored in a knowledge-base KB can be used by Chase algorithm (see [4], [6]) to impute missing attribute values describing objects in S. In this paper we show how to identify these objects and what additional values in S have to be hidden from users to guarantee that initially hidden attribute values in S can not be properly predicted by Distributed Chase.