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Objective: To enable privacy-preserving learning of high quality generative and discriminative machine learning models from distributed electronic health records. Methods and Results: We describe general and scalable strategy to build machine learning models in a provably privacy-preserving way. Compared to the standard approaches using, e.g., differential privacy, our method does not require alteration of the input biomedical data, works with completely or partially distributed datasets, and is resilient as long as the majority of the sites participating in data processing are trusted to not collude. We show how the proposed strategy can be applied on distributed medical records to solve the variables assignment problem, the key task in exact feature selection and Bayesian networks learning. Conclusions: Our proposed architecture can be used by health care organizations, spanning providers, insurers, researchers and computational service providers, to build robust and high quality predictive models in cases where distributed data has to be combined without being disclosed, altered or otherwise compromised.

In this work, we use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players' in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge. Our findings show that there are no apparent pandemic behavior changes, but some outliers were more likely to exhibit anti-social behavior (e.g., player killing). We also found that contrary to the reassuring adage that "Even if I knew the world would go to pieces tomorrow, I would still plant my apple tree," players abandoned character progression, showing a drastic decrease in quest completion, leveling, and ability changes at the end of the beta test., Comment: 10 pages, 9 figures, In Proceedings of the 26th International World Wide Web Conference (WWW) 2017

The domain name system (DNS) is an important protocol in today's Internet operation, and is the standard naming convention between domain names, names that are easy to read, understand, and remember by humans, to IP address of Internet resources. The wealth of research activities on DNS in general and security and privacy in particular suggest that all problems in this domain are solved. Reality however is that despite the large body of literature on various aspects of DNS, there are still many challenges that need to be addressed. In this paper, we review the various activities in the research community on DNS operation, security, and privacy, and outline various challenges and open research directions that need to be tackled.

As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber money in online games, can be monetized into real currency. The aim of this study is to detect game bots in a Massively Multiplayer Online Role Playing Game (MMORPG). We observed the behavioral characteristics of game bots and found that they execute repetitive tasks associated with gold farming and real money trading. We propose a game bot detection methodology based on user behavioral characteristics. The methodology of this paper was applied to real data provided by a major MMORPG company. Detection accuracy rate increased to 96.06% on the banned account list.