After the protocol ends, value of a data source's

This work-in-progress paper describes attempted improvements on Pachet's Description-Based Design (DBD), a system that uses machine learning to generate melodies. We discuss in depth both Description-Based Design and our extensions to Pachet's original approach. We also present a user study in which users had some success in transforming melodies and describe the implications of these results for future work.

We propose a new input perturbation mechanism for publishing a covariance matrix to achieve (epsilon,0)-differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, we apply this mechanism to principal component analysis (PCA). Our mechanism is able to keep the positive semi-definiteness of the published covariance matrix. Thus, our approach gives rise to a general publishing framework for input perturbation of a symmetric positive semidefinite matrix. Moreover, compared with the classic Laplace mechanism, our method has better utility guarantee. To the best of our knowledge, the Wishart mechanism is the best input perturbation approach for (epsilon,0)-differentially private PCA. We also compare our work with previous exponential mechanism algorithms in the literature and provide near optimal bound while having more flexibility and less computational intractability.

We propose a new input perturbation mechanism for publishing a covariance matrix to achieve $(\epsilon,0)$-differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, We apply this mechanism to principal component analysis. Our mechanism is able to keep the positive semi-definiteness of the published covariance matrix. Thus, our approach gives rise to a general publishing framework for input perturbation of a symmetric positive semidefinite matrix. Moreover, compared with the classic Laplace mechanism, our method has better utility guarantee. To the best of our knowledge, Wishart mechanism is the best input perturbation approach for $(\epsilon,0)$-differentially private PCA. We also compare our work with previous exponential mechanism algorithms in the literature and provide near optimal bound while having more flexibility and less computational intractability.