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The Algorithmic Foundations of Differential Privacy
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the ...
End-User Privacy in Human-Computer Interaction
Surveys the rich and diverse landscape of privacy in HCI and CSCW, describing some of the legal foundations and historical aspects of privacy, sketching out an overview of the body of knowledge with respect to designing, implementing, and evaluating privacy-affecting systems, and charting many directions for future work.
Data Streams
In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges.
Learning Deep Architectures for AI
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Human-robot Interaction
Presents a unified treatment of HRI-related issues, identifies key themes, and discusses challenge problems that are likely to shape the field in the near future. The survey includes research results from a cross section of the universities, government efforts, industry labs, and countries that contribute to HRI.
Deep Learning
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
Gossip Algorithms
A systematic survey of many of these recent results on Gossip network algorithms.
Architecture of a Database System
Architecture of a Database System presents an architectural discussion of DBMS design principles, including process models, parallel architecture, storage system design, transaction system implementation, query processor and optimizer architectures, and typical shared components and utilities.
Current Practices and Future Trends in Deep Foundations
GSP 125 contains 26 papers on state-of-the-art developments in deep foundation collected in honor of George G. Goble, Ph.D., P.E.
Group Testing
Researchers and students in modern communication and network systems will find this monograph an essential resource in understanding this new family of codes that will have a significant impact on such systems in the years to come.
