Abstract
Preserving confidentiality of individuals in data disclosure is a prime concern for public and private organizations. The main challenge in the data disclosure problem is to release data such that misuse by intruders is avoided while providing useful information to legitimate users for analysis. We propose an information theoretic architecture for the data disclosure problem. The proposed framework consists of developing a maximum entropy (ME) model based on statistical information of the actual data, testing the adequacy of the ME model, producing disclosure data from the ME model and quantifying the discrepancy between the actual and the disclosure data. The architecture can be used both for univariate and multivariate data disclosure. We illustrate the implementation of our approach using financial data.