IBM

About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Cache. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.

Holographic data storage

Abstract: We present an overview of our research effort on volume holographic digital data storage. Innovations, developments, and new insights gained in the design and operation of working storage platforms, novel optical components and techniques, data coding and signal processing algorithms, systems tradeoffs, materials testing and tradeoffs, and photon-gated storage materials are summarized.

Universal Hash Proofs and a Paradigm for Adaptive Chosen Ciphertext Secure Public-Key Encryption

Abstract: We present several new and fairly practical public-key encryption schemes and prove them secure against adaptive chosen ciphertext attack. One scheme is based on Paillier's Decision Composite Residuosity assumption, while another is based in the classical Quadratic Residuosity assumption. The analysis is in the standard cryptographic model, i.e., the security of our schemes does not rely on the Random Oracle model. Moreover, we introduce a general framework that allows one to construct secure encryption schemes in a generic fashion from language membership problems that satisfy certain technical requirements. Our new schemes fit into this framework, as does the Cramer-Shoup scheme based on the Decision Diffie-Hellman assumption.

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Abstract: Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack and significantly outperforms existing black-box attacks via substitute models.

P4p: provider portal for applications

Abstract: As peer-to-peer (P2P) emerges as a major paradigm for scalable network application design, it also exposes significant new challenges in achieving efficient and fair utilization of Internet network resources. Being largely network-oblivious, many P2P applications may lead to inefficient network resource usage and/or low application performance. In this paper, we propose a simple architecture called P4P to allow for more effective cooperative traffic control between applications and network providers. We conducted extensive simulations and real-life experiments on the Internet to demonstrate the feasibility and effectiveness of P4P. Our experiments demonstrated that P4P either improves or maintains the same level of application performance of native P2P applications, while, at the same time, it substantially reduces network provider cost compared with either native or latency-based localized P2P applications.