Privacy Preserving Distributed Data Mining

Privacy preserving distributed data mining aims to design secure protocols which allow multiple parties to conduct collaborative data mining while protecting the data privacy. My research focuses on the design and implementation of privacy preserving two-party protocols based on homomorphic encryption. I present new results in this area, including new secure protocols for basic operations and two fundamental privacy preserving data mining protocols. I propose a number of secure protocols for basic operations in the additive secret-sharing scheme based on homomorphic encryption. I derive a basic relationship between a secret number and its shares, with which we develop efficient secure comparison and secure division with public divisor protocols. I also design a secure inverse square root protocol based on Newton\u27s iterative method and hence propose a solution for the secure square root problem. In addition, we propose a secure exponential protocol based on Taylor series expansions. All these protocols are implemented using secure multiplication and can be used to develop privacy preserving distributed data mining protocols. In particular, I develop efficient privacy preserving protocols for two fundamental data mining tasks: multiple linear regression and EM clustering. Both protocols work for arbitrarily partitioned datasets. The two-party privacy preserving linear regression protocol is provably secure in the semi-honest model, and the EM clustering protocol discloses only the number of iterations. I provide a proof-of-concept implementation of these protocols in C++, based on the Paillier cryptosystem

Molecular subgroups of adult medulloblastoma: a long-term single-institution study

Background Recent transcriptomic approaches have demonstrated that there are at least 4 distinct subgroups in medulloblastoma (MB); however, survival studies of molecular subgroups in adult MB have been inconclusive because of small sample sizes. The aim of this study is to investigate the molecular subgroups in adult MB and identify their clinical and prognostic implications in a large, single-institution cohort. Methods We determined gene expression profiles for 13 primary adult MBs. Bioinformatics tools were used to establish distinct molecular subgroups based on the most informative genes in the dataset. Immunohistochemistry with subgroup-specific antibodies was then used for validation within an independent cohort of 201 formalin-fixed MB tumors, in conjunction with a systematic analysis of clinical and histological characteristics. Results Three distinct molecular variants of adult MB were identified: the SHH, WNT, and group 4 subgroups. Validation of these subgroups in the 201-tumor cohort by immunohistochemistry identified significant differences in subgroup-specific demographics, histology, and metastatic status. The SHH subgroup accounted for the majority of the tumors (62%), followed by the group 4 subgroup (28%) and the WNT subgroup (10%). Group 4 tumors had significantly worse progression-free and overall survival compared with tumors of the other molecular subtypes. Conclusions We have identified 3 subgroups of adult MB, characterized by distinct expression profiles, clinical features, pathological features, and prognosis. Clinical variables incorporated with molecular subgroup are more significantly informative for predicting adult patient outcome

Abstract Wavelet-Based Data Distortion for Privacy-Preserving Collaborative Analysis

With the rapid development of modern data collection and data warehouse technologies, data mining is becoming more and more a standard practice. Accompanying this trend, preserving privacy in certain data becomes a challenge to data mining applications in many fields, especially in medical, financial and homeland security fields. We present a class of novel privacy-preserving data distortion methods in the collaborative analysis situations based on wavelet transformation, which provides an effective and efficient balance between data utilities and privacy protection beyond its fast run time. We also provide a new privacy breach algorithm in the collaborative analysis which could threaten the data privacy, even with the distorted data values, in the single basis wavelet transformation case. Thus, we further propose a multi-basis wavelet data distortion strategy for better privacy preserving in these situations. Through experiments on real-life datasets, we conclude that the multi-basis wavelet data distortion method is a very promising privacy-preserving technique

Electroreduction of CO2 toward High Current Density

Carbon dioxide (CO2) electroreduction offers an attractive pathway for converting CO2 to valuable fuels and chemicals. Despite the existence of some excellent electrocatalysts with superior selectivity for specific products, these reactions are conducted at low current densities ranging from several mA cm−2 to tens of mA cm−2, which are far from commercially desirable values. To extend the applications of CO2 electroreduction technology to an industrial scale, long-term operations under high current densities (over 200 mA cm−2) are desirable. In this paper, we review recent major advances toward higher current density in CO2 reduction, including: (1) innovations in electrocatalysts (engineering the morphology, modulating the electronic structure, increasing the active sites, etc.); (2) the design of electrolyzers (membrane electrode assemblies, flow cells, microchannel reactors, high-pressure cells, etc.); and (3) the influence of electrolytes (concentration, pH, anion and cation effects). Finally, we discuss the current challenges and perspectives for future development toward high current densities

SegEQA : video segmentation based visual attention for embodied question answering

Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user's questions by exploring the real world environment. It has attracted increasing research interests due to its broad applications in automatic driving system, in-home robots, and personal assistants. Most of the existing methods perform poorly in terms of answering and navigation accuracy due to the absence of local details and vulnerability to the ambiguity caused by complicated vision conditions. To tackle these problems, we propose a segmentation based visual attention mechanism for Embodied Question Answering. Firstly, We extract the local semantic features by introducing a novel high-speed video segmentation framework. Then by the guide of extracted semantic features, a bottom-up visual attention mechanism is proposed for the Visual Question Answering (VQA) sub-task. Further, a feature fusion strategy is proposed to guide the training of the navigator without much additional computational cost. The ablation experiments show that our method boosts the performance of VQA module by 4.2% (68.99% vs 64.73%) and leads to 3.6% (48.59% vs 44.98%) overall improvement in EQA accuracy.AI SingaporeMinistry of Education (MOE)National Research Foundation (NRF)Accepted versionThe authors would like to thank the financial support from the program of China Scholarships Council (No.201806840059). This work is partly sup­ported by the National Research Foundation Singapore un­der its AI Singapore Programme [AISG-RP-2018-003] and the MOE Tier-I research grant [RG126/17 (S)]. We would like to thank NVIDIA for GPU donation. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore