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Jaeger J.,University of California at San Diego | Ristenpart T.,Cornell Technology | Tang Q.,Cornell University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Juels and Ristenpart introduced honey encryption (HE) and showed how to achieve message recovery security even in the face of attacks that can exhaustively try all likely keys. This is important in contexts like password-based encryption where keys are very low entropy, and HE schemes based on the JR construction were subsequently proposed for use in password management systems and even long-term protection of genetic data. But message recovery security is in this setting, like previous ones, a relatively weak property, and in particular does not prohibit an attacker from learning partial information about plaintexts or from usefully mauling ciphertexts. We show that one can build HE schemes that can hide partial information about plaintexts and that prevent mauling even in the face of exhaustive brute force attacks. To do so, we introduce target distribution semantic-security and target-distribution non-malleability security notions. We prove that a slight variant of the JR HE construction can meet them. The proofs require new balls-and-bins type analyses significantly different from those used in prior work. Finally, we provide a formal proof of the folklore result that an unbounded adversary which obtains a limited number of encryptions of known plaintexts can always succeed at message recovery. © International Association for Cryptologic Research 2016. Source

Marino B.,Cornell Technology | Juels A.,New York Institute of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Often, we wish to let parties alter or undo a contract that has been made. Toward this end, contract law has developed a set of traditional tools for altering and undoing contracts. Unfortunately, these tools often fail when applied to smart contracts. It is therefore necessary to define a new set of standards for the altering and undoing of smart contracts. These standards might ensure that the tools we use to alter and undo smart contracts achieve their original (contract law) goals when applied to this new technology. This paper develops such a set of standards and, then, to prove their worth as a framework, applies to them to an existing smart contract platform (Ethereum). © Springer International Publishing Switzerland 2016. Source

Juels A.,Cornell Technology
Proceedings of ACM Symposium on Access Control Models and Technologies, SACMAT

Decoy objects, often labeled in computer security with the term honey, are a powerful tool for compromise detection and mitigation. There has been little exploration of overarching theories or set of principles or properties, however. This short paper (and accompanying keynote talk) briefly explore two properties of honey systems, indistinguishability and secrecy. The aim is to illuminate a broad design space that might encompass a wide array of areas in information security, including access control, the main topic of this symposium. Source

Merolla P.A.,IBM | Arthur J.V.,IBM | Alvarez-Icaza R.,IBM | Cassidy A.S.,IBM | And 16 more authors.

Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts. Source

Moghimi M.,University of California at San Diego | Azagra P.,University of Zaragoza | Montesano L.,University of Zaragoza | Murillo A.C.,University of California at San Diego | And 2 more authors.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

This work describes and explores novel steps towards activity recognition from an egocentric point of view. Activity recognition is a broadly studied topic in computer vision, but the unique characteristics of wearable vision systems present new challenges and opportunities. We evaluate a challenging new publicly available dataset that includes trajectories of different users across two indoor environments performing a set of more than 20 different activities. The visual features studied include compact and global image descriptors, including GIST and a novel skin segmentation based histogram signature, and state-of-the art image representations for recognition, including Bag of SIFT words and Convolutional Neural Network (CNN) based features. Our experiments show that simple and compact features provide reasonable accuracy to obtain basic activity information (in our case, manipulation vs. non-manipulation). However, for finer grained categories CNN-based features provide the most promising results. Future steps include integration of depth information with these features and temporal consistency into the pipeline. © 2014 IEEE. Source

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