Smart contracts are applications that execute on blockchains. Today they manage billions of dollars in value and motivate visionary plans for pervasive blockchain deployment. While smart contracts inherit the availability and other security assurances of blockchains, however, they are impeded by blockchains' lack of confidentiality and poor performance. We present Ekiden, a system that addresses these critical gaps by combining blockchains with Trusted Execution Environments (TEEs). Ekiden leverages a novel architecture that separates consensus from execution, enabling efficient TEE-backed confidentiality-preserving smart-contracts and high scalability. Our prototype (with Tendermint as the consensus layer) achieves example performance of 600× more throughput and 400× less latency at 1000× less cost than the Ethereum mainnet. Another contribution of this paper is that we systematically identify and treat the pitfalls arising from harmonizing TEEs and blockchains. Treated separately, both TEEs and blockchains provide powerful guarantees, but hybridized, though, they engender new attacks. For example, in naïve designs, privacy in TEE-backed contracts can be jeopardized by forgery of blocks, a seemingly unrelated attack vector. We believe the insights learned from Ekiden will prove to be of broad importance in hybridized TEE-blockchain systems.

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv- GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.

Abstract An important line of privacy research is investigating the design of systems for secure input and output (I/O) within Internet browsers. These systems would allow for users’ information to be encrypted and decrypted by the browser, and the specific web applications will only have access to the users’ information in encrypted form. The state-of-the-art approach for a secure I/O system within Internet browsers is a system called ShadowCrypt created by UC Berkeley researchers [23]. This paper will explore the limitations of ShadowCrypt in order to provide a foundation for the general principles that must be followed when designing a secure I/O system within Internet browsers. First, we developed a comprehensive UI attack that cannot be mitigated with popular UI defenses, and tested the efficacy of the attack through a user study administered on Amazon Mechanical Turk. Only 1 of the 59 participants who were under attack successfully noticed the UI attack, which validates the stealthiness of the attack. Second, we present multiple attack vectors against Shadow-Crypt that do not rely upon UI deception. These attack vectors expose the privacy weaknesses of Shadow DOM—the key browser primitive leveraged by ShadowCrypt. Finally, we present a sketch of potential countermeasures that can enable the design of future secure I/O systems within Internet browsers.

This paper describes a unified power-frequency model (UPFM) which combines analytical and empirical approaches to ensure a high degree of modeling flexibility and accuracy to measured silicon (Si) results. On one end, System-on-a-Chip (SoC) design teams focus the bulk of their efforts on using detailed low-level models to verify power consumption. Such models are available late in the design cycle, and often limited in number of workloads that can be evaluated. On the other end, FPGA-based modeling and spreadsheet approaches that operate on higher-level abstraction have been proposed. However these are often limited by poor correlation to measured Si results. In addition, extant models typically focus on power projection or prediction of Si speed but not both. A unified approach is much needed since SOCs today have to meet stringent power and performance constraints simultaneously. The proposed UPFM model overcomes these limitations. First actual measured Si results serve as the empirical baseline foundation for projections so that simulated vs. measured differences can be calibrated. Second, each IP is analytically modeled using a large number of relevant parameters. This high level of abstraction allows for the model to be useful from early design cycle all the way to the mature phase (parameters get refined over time). Also, wide-ranging parameters have been carefully chosen (and improved over multiple product generations) so that accuracy is not sacrificed. We demonstrate UPFM as a comprehensive framework where technology, architecture and infrastructure (test/thermal) choices can be modeled with high accuracy and drive optimal perf-per-watt SoC designs.

A number of recent research and industry proposals discussed using encrypted data in web applications. We first present a systematization of the design space of web applications and highlight the advantages and limitations of current proposals. Next, we present ShadowCrypt, a previously unexplored design point that enables encrypted input/output without trusting any part of the web applications. ShadowCrypt allows users to transparently switch to encrypted input/output for text-based web applications. ShadowCrypt runs as a browser extension, replacing input elements in a page with secure, isolated shadow inputs and encrypted text with secure, isolated cleartext. ShadowCrypt's key innovation is the use of Shadow DOM, an upcoming primitive that allows low-overhead isolation of DOM trees. Evaluation results indicate that ShadowCrypt has low overhead and of practical use today. Finally, based on our experience with ShadowCrypt, we present a study of 17 popular web applications, across different domains, and the functionality impact and security advantages of encrypting the data they handle.