Knowledge Discovery From Sensitive Data: Differentially Private Bayesian Rule Lists

The utility of machine learning is rising, coming from a growing wealth of data and problems that are becoming harder to solve analytically. With these changes there is also the need for interpretable machine learning in order for users to understand how a machine learning algorithm comes to a specific output. Bayesian Rule Lists, an interpretable machine learning algorithm, offers an advanced accuracy to interpretabilty trade off when compared to other interpretable machine learning algorithms. Additionally, with the amount of data collected today, there is a lot of potentially sensitive data that we can learn from such as medical and criminal records. However, to do so, we must guarantee a degree of privacy on the dataset; differential privacy has become the standard for this private data analysis. In this paper, we propose a differentially private algorithm for Bayesian Rule Lists. We first break down the original Bayesian Rule List algorithm into three main components: frequent itemset mining, rule list sampling, and point estimate computation. We then perform a literature review to understand these algorithms, and ways to privatize them. There after we computed the necessary sensitivities for all subroutines, and ran experiments on the resulting differentially private algorithm to gauge utility. Results show that the proposed algorithm is able to output rule lists with good accuracy and decent interpretability

NEMO-binding domain peptide inhibits proliferation of human melanoma cells.

Melanoma is the most aggressive form of skin cancer, it originates from melanocytes and its incidence has increased in the last decade. Recent advances in the understanding of the underlying biology of the progression of melanoma have identified key signalling pathways that are important in promoting melanoma tumourigenesis, thus providing dynamic targets for therapy. One such important target identified in melanoma tumour progression is the Nuclear Factor-kB (NF-kB) pathway. In vitro studies have shown that NF-kB binding is constitutively elevated in human melanoma cultures compared to normal elanocytes. It has been found that a short cell-permeable peptide spanning the IKK-beta NBD, named NBD peptide, disrupted the association of NEMO with IKKs in vitro and blocked TNFalpha-induced NF-kB activation in vivo. In the present study we investigated the effect of the NBD peptide on NF-kB activity and survival of A375 human melanoma cells. We found that NBD peptide is able to inhibit the proliferation of A375 cells, which present constitutively elevated NF-kB levels. Inhibition of cell proliferation by NBD peptide was associated with direct inhibition of constitutive NF-kB DNA-binding activity and induction of apoptosis by activation of caspase-3 as confirmed by the cleavage and consequently inactivation of poly (ADP ribose) polymerase (PARP-1) known as the best marker of this process