Besides, to deal with complicated real-world scenarios where different kinds of partitioned data are involved, we propose a comprehensive schema that can work for both horizontally and vertically partitioned data models.
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With a series of carefully-designed algorithms, each participating party collaborates to build an ensemble of isolation trees efficiently without disclosing sensitive information of data. To achieve the goal, PIF makes an innovative improvement to the traditional iForest algorithm, enabling it in distributed environments. In this work, we propose PIF (Privacy-preserving Isolation Forest), which can detect outliers for multiple distributed data providers with high efficiency and accuracy while giving certain security guarantees. With the tremendous volume of data becoming more distributed than ever, global outlier detection for a group of distributed datasets is particularly desirable. The ability to detect outliers is crucial in data mining, with widespread usage in many fields, including fraud detection, malicious behavior monitoring, health diagnosis, etc. We validate our approaches on real-world data. To help users make decisions before disclosing any data, we use machine learning to predict the degree to which a user would benefit from collaborative learning. Thus, they choose between approaches in order to achieve their goals of predictive accuracy while minimizing the shared data. In contrast to previous work where users shared all their data with the centralized learner, we consider users that aim to preserve their privacy. A novel aspect of our work is that we carefully track the temporal evolution of the data available to the learner and the data shared by the user. In this paper, we use state-of-the-art deep neural network-based techniques to learn predictive human activity models in the local, centralized, and federated learning settings. However, disclosing the daily activities of an elderly or disabled user raises privacy concerns. To speed up the learning, several researchers designed collaborative learning systems that use data from multiple users. The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior.
He received the IEEE TCCC Outstanding Service Award in 2009 and was the recipient of the 2010 INFOCOM Achievement Award for pioneering contributions to the theory and practice of QoS in networks. He is an ACM and IEEE Fellow and is currently serving as the Chair of ACM SIGCOMM. from Caltech and did his undergraduate at ENST in France. He was on leave from Penn between 20, starting Ipsum Networks, a company that pioneered the concept of route analytics for managing IP networks. Watson Research Center in a variety of technical and management positions. Prior to joining Penn, he spent 12 years at the IBM T. He previously was the Alfred Fitler Moore Professor of Telecommunications Networks in the Electrical and System Engineering department of the University of Pennsylvania, which he joined in October 1998. Welge Professor and Chair of Computer Science and Engineering at Washington University in Saint Louis, which he joined in 2013.
I will then use examples from a few ongoing projects to illustrate how the “new” and the “old” can combine as part of modern networking research, and while I won’t venture into predicting the future I’ll offer opinions on what I consider promising directions.īiography: Roch Guérin is the Harold B. I will use this information together with experience from my own career in industry and academia to extract trends and perspectives on the evolution of the networking research landscape. Data from papers published at INFOCOM since its inception offer a glimpse into the evolution of topics that have fueled the growth of networking and the tools used to tackle them.
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The combination of a wealth of data from publications and personal experience offers an opportunity for an analysis of the path that has taken us where we are today, and for possible lessons on where we might be heading and how to continue what has arguably been an amazing trajectory. While my own perspective on the topic does not span that many years, it is close. INFOCOM itself is celebrating its 40th birthday this year. Abstract: Data/packet networks that make-up today’s ubiquitous communication infrastructure are close to 60 years old, and conferences devoted to the topic are approaching that age.