Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach

Virtual screening (VS) is widely used during computational drug discovery to reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to predict new compound-protein interactions (CPIs) from known CPI network data using several methods, including machine learning and data mining. Although CGBVS facilitates highly efficient and accurate CPI prediction, it has poor performance for prediction of new compounds for which CPIs are unknown. The pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high accuracy for prediction of new compounds. In this study, on the basis of link mining, we improved the PKM by combining link indicator kernel (LIK) and chemical similarity and evaluated the accuracy of these methods. The proposed method obtained an average area under the precision-recall curve (AUPR) value of 0.562, which was higher than that achieved by the conventional Gaussian interaction profile (GIP) method (0.425), and the calculation time was only increased by a few percent

Secure Computation from Elastic Noisy Channels

Noisy channels enable unconditionally secure multi-party computation even against parties with unbounded computational power. But inaccurate noise estimation and adversarially determined channel characteristics render known protocols insecure. Such channels are known as unreliable noisy channels. A large body of work in the last three decades has attempted to construct secure multi-party computation from unreliable noisy channels, but this previous work has not been able to deal with most parameter settings. In this work, we study a form of unreliable noisy channels where the unreliability is one-sided, that we name elastic noisy channels: thus, in one form of elastic noisy channel, an adversarial receiver can increase the reception reliability unbeknown to the sender, but the sender cannot change the channel characteristic. Our work shows feasibility results for a large set of parameters for the elastic binary symmetric channel, significantly improving upon the best results obtainable using prior techniques. In a key departure from existing approaches, we use a more elemental correlated private randomness as an intermediate cryptographic primitive that exhibits only a rudimentary essence of oblivious transfer. Toward this direction, we introduce new information-theoretic techniques that are potentially applicable to other cryptographic settings involving unreliable noisy channels

Protecting Privacy of Users in Brain-Computer Interface Applications.

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations