Biblio

In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.

Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.

Outsourcing services to third-party providers comes with a high security cost-to fully trust the providers. Using trusted hardware can help, but current trusted execution environments do not adequately support services that process very large scale datasets. We present LASTGT, a system that bridges this gap by supporting the execution of self-contained services over a large state, with a small and generic trusted computing base (TCB). LASTGT uses widely deployed trusted hardware to guarantee integrity and verifiability of the execution on a remote platform, and it securely supplies data to the service through simple techniques based on virtual memory. As a result, LASTGT is general and applicable to many scenarios such as computational genomics and databases, as we show in our experimental evaluation based on an implementation of LAST-GT on a secure hypervisor. We also describe a possible implementation on Intel SGX.

Smart contracts are programs that execute autonomously on blockchains. Their key envisioned uses (e.g. financial instruments) require them to consume data from outside the blockchain (e.g. stock quotes). Trustworthy data feeds that support a broad range of data requests will thus be critical to smart contract ecosystems. We present an authenticated data feed system called Town Crier (TC). TC acts as a bridge between smart contracts and existing web sites, which are already commonly trusted for non-blockchain applications. It combines a blockchain front end with a trusted hardware back end to scrape HTTPS-enabled websites and serve source-authenticated data to relying smart contracts. TC also supports confidentiality. It enables private data requests with encrypted parameters. Additionally, in a generalization that executes smart-contract logic within TC, the system permits secure use of user credentials to scrape access-controlled online data sources. We describe TC's design principles and architecture and report on an implementation that uses Intel's recently introduced Software Guard Extensions (SGX) to furnish data to the Ethereum smart contract system. We formally model TC and define and prove its basic security properties in the Universal Composibility (UC) framework. Our results include definitions and techniques of general interest relating to resource consumption (Ethereum's "gas" fee system) and TCB minimization. We also report on experiments with three example applications. We plan to launch TC soon as an online public service.

Malicious hardware is a realistic threat. It can be possible to insert the malicious functionality on a device as deep as in the hardware design flow, long before manufacturing the silicon product. Towards developing a hardware Trojan horse detection methodology, we analyze capabilities and limitations of existing techniques, framing a testing strategy for uncovering efficiently hardware Trojan horses in mass-produced integrated circuits.