Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this article, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract executionwithout hard-forks. Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions.

Talek is a private group messaging system that sends messages through potentially untrustworthy servers, while hiding both data content and the communication patterns among its users. Talek explores a new point in the design space of private messaging; it guarantees access sequence indistinguishability, which is among the strongest guarantees in the space, while assuming an anytrust threat model, which is only slightly weaker than the strongest threat model currently found in related work. Our results suggest that this is a pragmatic point in the design space, since it supports strong privacy and good performance: we demonstrate a 3-server Talek cluster that achieves throughput of 9,433 messages/second for 32,000 active users with 1.7-second end-to-end latency. To achieve its security goals without coordination between clients, Talek relies on information-theoretic private information retrieval. To achieve good performance and minimize server-side storage, Talek introduces new techniques and optimizations that may be of independent interest, e.g., a novel use of blocked cuckoo hashing and support for private notifications. The latter provide a private, efficient mechanism for users to learn, without polling, which logs have new messages.

Nowadays an emerging class of applications are based oncollaboration over a shared database among different entities. However, the existing solutions on shared database may require trust on others, have high hardware demand that is unaffordable for individual users, or have relatively low performance. In other words, there is a trilemma among security, compatibility and efficiency. In this paper, we present FalconDB, which enables different parties with limited hardware resources to efficiently and securely collaborate on a database. FalconDB adopts database servers with verification interfaces accessible to clients and stores the digests for query/update authentications on a blockchain. Using blockchain as a consensus platform and a distributed ledger, FalconDB is able to work without any trust on each other. Meanwhile, FalconDB requires only minimal storage cost on each client, and provides anywhere-available, real-time and concurrent access to the database. As a result, FalconDB over-comes the disadvantages of previous solutions, and enables individual users to participate in the collaboration with high efficiency, low storage cost and blockchain-level security guarantees.

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.

In this work, we demonstrate Sterling, a decentralized marketplace for private data. Sterling enables privacy-preserving distribution and use of data by using privacy-preserving smart contracts which run on a permissionless blockchain. The privacy-preserving smart contracts, written by data providers and consumers, immutably and irrevocably represent the interests of their creators. In particular, we provide a mechanism for data providers to control the use of their data through automatic verification of data consumer contracts, allowing providers to express constraints such as pricing and differential privacy. Through smart contracts and trusted execution environments, Sterling enables privacy-preserving analytics and machine learning over private data in an efficient manner. The resulting economy ensures that the interests of all parties are aligned. For the demonstration, we highlight the use of Sterling for training machine learning models on individuals' health data. In doing so, we showcase novel approaches to automatically appraising training data, verifying and enforcing model privacy properties, and efficiently training private models on the blockchain using trusted hardware.

Web applications are a frequent target of successful attacks. In most web frameworks, the damage is amplified by the fact that application code is responsible for security enforcement. In this paper, we design and evaluate Radiatus, a shared-nothing web framework where application-specific computation and storage on the server is contained within a sandbox with the privileges of the end-user. By strongly isolating users, user data and service availability can be protected from application vulnerabilities.

Kineograph is a distributed system that takes a stream of incoming data to construct a continuously changing graph, which captures the relationships that exist in the data feed. As a computing platform, Kineograph further supports graph-mining algorithms to extract timely insights from the fast-changing graph structure. To accommodate graph-mining algorithms that assume a static underlying graph, Kineograph creates a series of consistent snapshots, using a novel and efficient epoch commit protocol. To keep up with continuous updates on the graph, Kineograph includes an incremental graph-computation engine. We have developed three applications on top of Kineograph to analyze Twitter data: user ranking, approximate shortest paths, and controversial topic detection. For these applications, Kineograph takes a live Twitter data feed and maintains a graph of edges between all users and hashtags. Our evaluation shows that with 40 machines processing 100K tweets per second, Kineograph is able to continuously compute global properties, such as user ranks, with less than 2.5-minute timeliness guarantees. This rate of traffic is more than 10 times the reported peak rate of Twitter as of October 2011.

Free web services often face growing pains. In the current client-server access model, the cost of providing a service increases with its popularity. This leads organizations that want to provide services free-of-charge to rely to donations, advertisements, or mergers with larger companies to cope with operational costs.