Constrained Nonnegative Matrix Factorization for Data Privacy

The amount of data that is being produced has increased rapidly so has the various data mining methods with the aim of discovering hidden patterns and knowledge in the data. With this has raised the problem of confidential data being disclosed. This paper is an effort to not let those confidential data be disclosed. We apply constrained nonnegative matrix factorization (NMF) in order to achieve what is also known as dual privacy protection that accounts for both the data and pattern hiding, though in this paper, we mainly focus on pattern hiding. To add the constraint we change the update rule as well as the objective function in NMF computation. As the procedure reaches the convergence, it yields a new dataset, which suppresses the patterns that are considered confidential. The effectiveness of this novel hiding technique is examined on two benchmark datasets (IRIS and YEAST). We show that, an optimal solution can be computed in which the user specified confidential memberships or relationships are hidden without undesirable alterations on non-confidential patterns, also referred to as side effects in this paper. This paper presents our idea of how the different parameters will vary to achieve convergence.