Chain: A Dynamic Double Auction Framework for Matching Patient Agents

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile

Redesigning Bitcoin's fee market

The security of the Bitcoin system is based on having a large amount of computational power in the hands of honest miners. Such miners are incentivized to join the system and validate transactions by the payments issued by the protocol to anyone who creates blocks. As new bitcoins creation rate decreases (halving every 4 years), the revenue derived from transaction fees start to have an increasingly important role. We argue that Bitcoin's current fee market does not extract revenue well when blocks are not congested. This effect has implications for the scalability debate: revenue from transaction fees may decrease if block size is increased. The current mechanism is a "pay your bid" auction in which included transactions pay the amount they suggested. We propose two alternative auction mechanisms: The Monopolistic Price Mechanism, and the Random Sampling Optimal Price Mechanism (due to Goldberg et al.). In the monopolistic price mechanism, the miner chooses the number of accepted transactions in the block, and all transactions pay exactly the smallest bid included in the block. The mechanism thus sets the block size dynamically (up to a bound required for fast block propagation and other security concerns). We show, using analysis and simulations, that this mechanism extracts revenue better from users, and that it is nearly incentive compatible: the profit due to strategic bidding relative to honest biding decreases as the number of bidders grows. Users can then simply set their bids truthfully to exactly the amount they are willing to pay to transact, and do not need to utilize fee estimate mechanisms, do not resort to bid shading and do not need to adjust transaction fees (via replace-by-fee mechanisms) if the mempool grows. We discuss these and other properties of our mechanisms, and explore various desired properties of fee market mechanisms for crypto-currencies

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

Efficient Concurrent Execution of Smart Contracts in Blockchains using Object-based Transactional Memory

This paper proposes an efficient framework to execute Smart Contract Transactions (SCTs) concurrently based on object semantics, using optimistic Single-Version Object-based Software Transactional Memory Systems (SVOSTMs) and Multi-Version OSTMs (MVOSTMs). In our framework, a multi-threaded miner constructs a Block Graph (BG), capturing the object-conflicts relations between SCTs, and stores it in the block. Later, validators re-execute the same SCTs concurrently and deterministically relying on this BG. A malicious miner can modify the BG to harm the blockchain, e.g., to cause double-spending. To identify malicious miners, we propose Smart Multi-threaded Validator (SMV). Experimental analysis shows that the proposed multi-threaded miner and validator achieve significant performance gains over state-of-the-art SCT execution framework.Comment: 49 pages, 26 figures, 11 table