Boyuan Feng, Zheng Wang, Yuke Wang, Shu Yang · 5 authors
Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.
Konstantinos Sgantzos, Mohamed Al Hemairy, Panagiotis Tzavaras, Spyridon Stelios
The usage of Large Language Models (LMMs) and their exponential progress has created a Cambrian Explosion in the development of new tools for almost every field of science and technology, but also presented significant concerns regarding the AI ethics and creation of sophisticated malware and phishing attacks. Moreover, several worries have arisen in the field of dataset collection and intellectual property in that many datasets may exist without the license of the respective owners. Triple-Entry Accounting (TEA) has been proposed by Ian Grigg to increase transparency, accountability, and security in financial transactions. This method expands upon the traditional double-entry accounting system, which records transactions as debits and credits in two separate ledgers, by incorporating a third ledger as an independent verifier via a digitally signed receipt. The utilization of a digital signature provides evidentiary power to the receipt, thus reducing the accounting problem to one of the presence or absence of the receipt. The integrity issues associated with double-entry accounting can be addressed by allowing the parties involved in the transaction to share the records with an external auditor. This manuscript proposes a novel methodology to apply triple-entry accounting records on a publicly accessed distributed ledger technology medium to control the queries of LLMs in order to discourage malicious acts and ensure intellectual property rights.
Ning Lin, Shaocong Wang, Yi Li, Bo Wang · 22 authors
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.
Ali Nikhalat-Jahromi, Ali Mohammad Saghiri, Mohammad Reza Meybodi
Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.
To protect participants’ confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.
Chao Ni, Cong Tian, Kaiwen Yang, David Lo · 6 authors
Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain’s characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The existing approaches to identify crash-inducing smart contracts are either limited in finding vulnerability (e.g., pattern-based static analysis) or very expensive (e.g., program analysis), which is insufficient for Ethereum.To reduce runtime errors on Ethereum, we propose an efficient, generalizable, and machine learning-based crash-inducing smart contract detector, CRASHSCDET, to automatically identify crash-inducing smart contracts. To investigate the effectiveness of CRASHSCDET, we firstly propose 34 static source code metrics from four dimensions (i.e., complexity metrics, count metrics, object-oriented metrics, and Solidity-specific metrics) to characterize smart contracts. Then, we collect a large-scale dataset of verified smart contracts (i.e., 54,739) and label these smart contracts based on their execution traces on Etherscan. We make a comprehensive comparison with three state-of-the-art approaches and the results show that CRASHSCDET can achieve good performance (i.e., 0.937 of F1-measure and 0.980 of AUC on average) and statistically significantly improve the baselines by 0.5%-60.4% in terms of F1-measure and by 41.2%-44.3% in terms of AUC, which indicates the effectiveness of static source code metrics in identifying crash-inducing smart contracts. We further investigate the importance of different types of metrics and find that metrics in different dimensions have varying abilities to depict the characteristic of smart contracts. Especially, metrics belonging to the "Count" dimension are the most discriminative ones but combining all metrics can achieve better prediction performance.
Deep neural networks (DNNs) could be very useful in blockchain applications such as DeFi and NFT trading. However, training / running large-scale DNNs as part of a smart contract is infeasible on today's blockchain platforms, due to two fundamental design issues of these platforms. First, blockchains nowadays typically require that each node maintain the complete world state at any time, meaning that the node must execute all transactions in every block. This is prohibitively expensive for computationally intensive smart contracts involving DNNs. Second, existing blockchain platforms expect smart contract transactions to have deterministic, reproducible results and effects. In contrast, DNNs are usually trained / run lock-free on massively parallel computing devices such as GPUs, TPUs and / or computing clusters, which often do not yield deterministic results. This paper proposes novel platform designs, collectively called A New Hope (ANH), that address the above issues. The main ideas are (i) computing-intensive smart contract transactions are only executed by nodes who need their results, or by specialized serviced providers, and (ii) a non-deterministic smart contract transaction leads to uncertain results, which can still be validated, though at a relatively high cost; specifically for DNNs, the validation cost can often be reduced by verifying properties of the results instead of their exact values. In addition, we discuss various implications of ANH, including its effects on token fungibility, sharding, private transactions, and the fundamental meaning of a smart contract.
Recently, the trending 5G technology encourages extensive applications of on-device machine learning , which collects user data for model training. This requires cost-effective techniques to preserve the privacy and the security of model training within the resource-constrained environment. Traditional learning methods rely on the trust among the system for privacy and security. However, with the increase of the learning scale, maintaining every edge device’s trustworthiness could be expensive. To cost-effectively establish trust in a trustless environment, this paper proposes democratic learning (DemL), which makes the first step to explore hardware/software co-design for blockchain-secured decentralized on-device learning. By utilizing blockchain’s decentralization and tamper-proofing, our design secures AI learning in a trustless environment. To tackle the extra overhead introduced by blockchain , we propose PoMC (an algorithm and architecture co-design) as a novel blockchain consensus mechanism , which first exploits cross-domain reuse (AI learning and blockchain consensus) in AI learning architecture. Evaluation results show our DemL can protect AI learning from privacy leakage and model pollution, and demonstrated that privacy and security come with trivial hardware overhead and power consumption (2%). We believe that our work will open the door of synergizing blockchain and on-device learning for security and privacy.
Haris Javaid, Ji Yang, Nathania Santoso, Mohit Upadhyay · 7 authors
In this paper, we demonstrate how Hyperledger Fabric, one of the most popular permissioned blockchains, can benefit from network-attached acceleration. The scalability and peak performance of Fabric is primarily limited by the bottlenecks present in its block validation/commit phase. We propose Blockchain Machine, a hardware accelerator coupled with a hardware-friendly communication protocol, to act as the validator peer. It can be adapted to applications and their smart contracts, and is targeted for a server with network-attached FPGA acceleration card. The Blockchain Machine retrieves blocks and their transactions in hardware directly from the network interface, which are then validated through a configurable and efficient block-level and transaction-level pipeline. The validation results are then transferred to the host CPU where non-bottleneck operations are executed. From our implementation integrated with Fabric v1.4 LTS, we observed up to 12x speedup in block validation when compared to software-only validator peer, with commit throughput of up to 68,900 tps. Our work provides an acceleration platform that will foster further research on hardware acceleration of permissioned blockchains.
Popular blockchains such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Serial execution of smart contract transactions/atomic-units (AUs) fails to harness the multiprocessing power offered by the prevalence of multi-core processors. By adding concurrency to the execution of AUs, we can achieve better efficiency and higher throughput. In this paper, we develop a concurrent miner that proposes a block by executing the AUs concurrently using optimistic Software Transactional Memory systems (STMs). It captures the independent AUs in a concurrent bin and dependent AUs in the block graph (BG) efficiently. Later, we propose a concurrent validator that re-executes the same AUs concurrently and deterministically using a concurrent bin followed by a BG given by the miner to verify the proposed block. We rigorously prove the correctness of concurrent execution of AUs and achieve significant performance gain over the state-of-the-art.
Christof Ferreira Torres, Antonio Ken Iannillo, Arthur Gervais, Radu State
<div> <div> <p>Smart contracts are Turing-complete programs that are executed across a blockchain. Unlike traditional programs, once deployed, they cannot be modified. As smart contracts carry more value, they become more of an exciting target for attackers. Over the last years, they suffered from exploits costing millions of dollars due to simple programming mistakes. As a result, a variety of tools for detecting bugs have been proposed. Most of these tools rely on symbolic execution, which may yield false positives due to over-approximation. Recently, many fuzzers have been proposed to detect bugs in smart contracts. However, these tend to be more effective in finding shallow bugs and less effective in finding bugs that lie deep in the execution, therefore achieving low code coverage and many false negatives. An alternative that has proven to achieve good results in traditional programs is hybrid fuzzing, a combination of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on smart contracts and present ConFuzzius, the first hybrid fuzzer for smart contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a smart contract and constraint solving to generate inputs that satisfy complex conditions that prevent evolutionary fuzzing from exploring deeper parts. Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently generate sequences of transactions that are more likely to result in contract states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius by comparing it with state-of-the-art symbolic execution tools and fuzzers for smart contracts. Our evaluation on a curated dataset of 128 contracts and a dataset of 21K real-world contracts shows that our hybrid approach detects more bugs than state-of-the-art tools (up to 23%) and that it outperforms existing tools in terms of code coverage (up to 69%). We also demonstrate that data dependency analysis can boost bug detection up to 18%.</p> </div> </div>
It is crucial that smart contracts are tested thoroughly due to their immutable nature. Even small bugs in smart contracts can lead to huge monetary losses. However, testing is not enough; it is also important to ensure the quality and completeness of the tests. There are already several approaches that tackle this challenge with mutation testing, but their effectiveness is questionable since they only considered small contract samples. Hence, we evaluate the quality of smart contract mutation testing at scale. We choose the most promising of the existing (smart contract specific) mutation operators, analyse their effectiveness in terms of killability and highlight severe vulnerabilities that can be injected with the mutations. Moreover, we improve the existing mutation methods by introducing a novel killing condition that is able to detect a deviation in the gas consumption, i.e., in the monetary value that is required to perform transactions. This paper has a replication package at https://github.com/pieterhartel/Mutation-at-scale
Popular blockchain such as Ethereum and several others execute complex transactions in blocks through user defined scripts known as smart contracts. Normally, a block of the chain consists of multiple transactions of smart contracts which are added by a miner. To append a correct block into blockchain, miners execute these smart contract transactions (SCT) sequentially. Later the validators serially re-execute the SCT of the block. In the current era of multi-core processors, by employing serial execution of the transactions, the miners and validators fail to utilize the cores properly and as a result have poor throughput. By adding concurrency using object semantics to smart contracts execution, we can achieve the better efficiency and higher throughput. Some authors have used read-write STMs (RWSTMs) for the concurrent execution of SCT. Working with higher level operations provide greater concurrency, better throughput and reduces the number of aborts than RWSTMs. In this paper, we develop an efficient framework to execute the SCT concurrently by miner using optimistic Object-Based Software Transactional Memory systems (OSTMs) and Multi-Version OSTMs (MV-OSTM). A proposed block includes SCT, final states of the shared data-items, hash of the previous block and a block graph (BG). BG captures the conflicting relations among the transactions. Later, the validators re-execute the same SCT concurrently and deterministically with the help of BG given by miner to verify the final state. If the validation is successful then proposed block appended into the blockchain and miner gets incentive otherwise discard the proposed block. MV-OSTM and OSTM miner performs 4.5x and 3.86x average speedups over serial miner. Along with, MV-OSTM and OSTM validator outperforms average 32.81x and 29.76x than serial validator.
It is commonly believed that blockchain is a revolutionary technology for doing business on the Internet. Blockchain is a decentralized, distributed database or ledger of records. It ensures that the records are tamper-proof but publicly readable. Blockchain platforms such as Ethereum [3] and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Normally, a block of the chain consists of multiple transactions of smart contracts which are added by a miner. To append a correct block into the blockchain, miners execute these transactions of smart contracts sequentially. Later the validators serially re-execute the smart contract transactions of the block. If the validators agree with final state of the blocks as recorded by the miner, then the block is said to be valid and added to the blockchain using a consensus protocol.
Fabrice Benhamouda, Stephan Krenn, Vadim Lyubashevsky, Krzysztof Pietrzak
Abstract. We design an efficient commitment scheme, and companion zero-knowledge proofs of knowledge, based on the learning with errors over rings (RLWE) problem. In particular, for rings in which almost all elements have inverses, we construct a perfectly binding commitment scheme whose hiding property relies on the RLWE assumption. Our scheme maps elements from the ring (or equivalently, n elements from Fq) to a small constant number of ring elements. We then construct Σ-protocols for proving, in a zero-knowledge manner, knowledge of the message contained in a commitment. We are able to further extend our basic protocol to allow us to prove additive and multiplicative relations among committed values. Our protocols have a communication complexity of O(Mn log q) and achieve a negligible knowledge error in one run. Here M is the constant from a rejection sampling technique that we employ, and can be set close to 1 by adjusting other parameters. Previously known Σ-protocols for LWE-related languages either relied on “smudging ” out the error (which necessitates working over large fields, resulting in poor efficiency) or only achieved a noticeable or even constant knowledge error (thus requiring many repetitions of the protocol).