2PC Protocol Research Articles

SIGMA: Secure GPT Inference with Function Secret Sharing

Secure 2-party computation (2PC) enables secure inference that offers protection for both proprietary machine learning (ML) models and sensitive inputs to them. However, the existing secure inference solutions suffer from high latency and communication overheads, particularly for transformers. Function secret sharing (FSS) is a recent paradigm for obtaining efficient 2PC protocols with a preprocessing phase. We provide Sigma, the first end-to-end system for secure transformer inference based on FSS. By constructing new FSS-based protocols for complex machine learning functionalities, such as Softmax, GeLU and SiLU, and also accelerating their computation on GPUs, Sigma improves the latency of secure inference of transformers by 11 - 19x over the state-of-the-art that uses preprocessing and GPUs. We present the first secure inference of generative pre-trained transformer (GPT) models. In particular, Sigma executes Meta's Llama2 (available on HuggingFace) with 13 billion parameters in 44 seconds and GPT2 in 1.6 seconds

ICOE: A Lightweight Group-Consensus-Based Off-Chain Execution Model for Smart Contract-Based Industrial Applications

Programmable smart contracts facilitate the application of blockchain to many areas of industry, e.g., supply chains, energy, and healthcare. The off-chain execution model for blockchain smart contracts is a promising approach to achieve scalability for industrial applications, which executes each smart contract in one of the small-scale consensus groups. However, previous work based on such a model cannot efficiently handle cross-group contract transactions that involve multiple cross-group invocations between smart contracts, because of the heavyweight state synchronization and repeated executions. In this paper, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> , a lightweight group-consensus based off-chain execution model for cross-group contract transactions without the need of state synchronization and duplicate executions, and also prove its correctness. The key novelty of our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> is to let the invoked smart contract only executed in its residing group and only return the execution result to the invoking smart contract, while leveraging the two-phase commit(2PC) protocol guarantees consistent commits across multiple groups within the same invoking smart contract transaction. We implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> and extensive experimental results show that our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE</small> achieves a 14x improvement of throughput on average compared to the state-of-the-art model.

An Adaptive Metadata Management Scheme Based on Deep Reinforcement Learning for Large-Scale Distributed File Systems

A major challenge confronting today’s distributed metadata management schemes is how to meet the dynamic requirements of various applications through effectively mapping and migrating metadata nodes to different metadata servers (MDS’s). Most of the existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make a timely and efficient update on the distribution of nodes. In this paper, we present the first deep reinforcement learning-leveraged distributed metadata management scheme, AdaM, to address the aforementioned dilemma. AdaM is an adaptive fine-grained metadata management scheme that trains an actor-critic network to migrate “hot” metadata nodes to different MDS’s based on its observations of the current “states” (i.e., access pattern, the structure of namespace tree and current distribution of nodes on MDS’s). Adaptive to varying access patterns, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. Besides, we propose a self-adaptive metadata cache policy, which dynamically combines the two strategies of managing caches on the server side and the client side to gain better query performance. Last but not least, we design a distributed metadata processing 2PC Protocol called MST-based 2PC to ensure data consistency. Experiments on a real-world dataset demonstrate the superiority of our proposed method over other schemes.

LLAMA: A Low Latency Math Library for Secure Inference

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both the client (holding sensitive input) and the server (holding sensitive weights of the ML model) while realizing inferenceas-a-service. Although many specialized protocols exist for this task, including those in the preprocessing model (where a majority of the overheads are moved to an input independent offline phase), they all still suffer from large online complexity. Specifically, the protocol phase that executes once the parties know their inputs, has high communication, round complexity, and latency. Function Secret Sharing (FSS) based techniques offer an attractive solution to this in the trusted dealer model (where a dealer provides input independent correlated randomness to both parties), and 2PC protocols obtained based on these techniques have a very lightweight online phase. Unfortunately, current FSS-based 2PC works (AriaNN, PoPETS 2022; Boyle et al. Eurocrypt 2021; Boyle et al. TCC 2019) fall short of providing a complete solution to secure inference. First, they lack support for math functions (e.g., sigmoid, and reciprocal square root) and hence, are insufficient for a large class of inference algorithms (e.g. recurrent neural networks). Second, they restrict all values in the computation to be of the same bitwidth and this prevents them from benefitting from efficient float-to-fixed converters such as Tensorflow Lite that crucially use low bitwidth representations and mixed bitwidth arithmetic. In this work, we present LLAMA – an end-to-end, FSS based, secure inference library supporting precise low bitwidth computations (required by converters) as well as provably precise math functions; thus, overcoming all the drawbacks listed above. We perform an extensive evaluation of LLAMA and show that when compared with non-FSS based libraries supporting mixed bitwidth arithmetic and math functions (SIRNN, IEEE S&amp;P 2021), it has at least an order of magnitude lower communication, rounds, and runtimes. We integrate LLAMA with the EzPC framework (IEEE EuroS&amp;P 2019) and demonstrate its robustness by evaluating it on large benchmarks (such as ResNet-50 on the ImageNet dataset) as well as on benchmarks considered in AriaNN – here too LLAMA outperforms prior work.

Cornus

Two-phase commit (2PC) is widely used in distributed databases to ensure atomicity of distributed transactions. Conventional 2PC was originally designed for the shared-nothing architecture and has two limitations: long latency due to two eager log writes on the critical path, and blocking of progress when a coordinator fails. Modern cloud-native databases are moving to a storage disaggregation architecture where storage is a shared highly-available service. Our key observation is that disaggregated storage enables protocol innovations that can address both the long-latency and blocking problems. We develop Cornus, an optimized 2PC protocol to achieve this goal. The only extra functionality Cornus requires is an atomic compare-and-swap capability in the storage layer, which many existing storage services already support. We present Cornus in detail and show how it addresses the two limitations. We also deploy it on real storage services including Azure Blob Storage and Redis. Empirical evaluations show that Cornus can achieve up to 1.9X latency reduction over conventional 2PC.

Early Conflict Checking in Two-Phase Commit Protocol for Replicated State Machines

Representing an algorithmic workflow as a state machine is a frequently used technique in distributed systems. Replicating a state machine in a fault tolerant way is one of the main application areas under this context. When implementing a replicated state machine, a crucial problem is to maintain consistency among replicas that might handle various different requests arriving at each different replica. This problem requires maintaining a single consistent ordering of the distributed requests handled separately by replicas. Basic consensus protocols such as two-phase commit (2PC) can be used to maintain consistency between replicas whenever a request is to be processed. In this study, the authors modify 2PC protocol to take advantage of basic properties of a state machine and detect possible write conflicts earlier. The experiments on distributed cloud environments show that the modified 2PC protocol increases the throughput and decreases wasted write operations by a significant amount.

Multi-Institution Encrypted Medical Imaging AI Validation Without Data Sharing

Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 chest x-ray diagnosis model was evaluated on multi-institutional chest radiographic imaging datasets both with and without CrypTFlow2 on two test sets spanning seven sites across the US and India, and comprising 1,149 chest x-ray images. We measure comparative AUROC performance between secure and insecure inference in multiple pathology classification tasks, and explore model output distributional shifts and resource constraints introduced by secure model inference. Secure inference with CrypTFlow2 demonstrated no significant difference in AUROC for all diagnoses, and model outputs from secure and insecure inference methods were distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf secure 2PC between healthcare systems and AI model vendors for medical imaging, without changes in performance, and can facilitate scalable pre-deployment infrastructure for real-world secure model evaluation without exposure to patient data or model IP.

Multi-Institution Encrypted Medical Imaging Ai Validation Without Data Sharing

Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 chest x-ray diagnosis model was evaluated on multi-institutional chest radiographic imaging datasets both with and without CrypTFlow2 on two test sets spanning seven sites across the US and India, and comprising 1,149 chest x-ray images. We measure comparative AUROC performance between secure and insecure inference in multiple pathology classification tasks, and explore model output distributional shifts and resource constraints introduced by secure model inference. Secure inference with CrypTFlow2 demonstrated no significant difference in AUROC for all diagnoses, and model outputs from secure and insecure inference methods were distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf secure 2PC between healthcare systems and AI model vendors for medical imaging, without changes in performance, and can facilitate scalable pre-deployment infrastructure for real-world secure model evaluation without exposure to patient data or model IP.

2PC*: a distributed transaction concurrency control protocol of multi-microservice based on cloud computing platform

The two-phase commit (2PC) protocol is a key technique for achieving distributed transactions in storage systems such as relational databases and distributed databases. 2PC is a strongly consistent and centralized atomic commit protocol that ensures the serialization of the transaction execution order. However, it does not scale well to large and high-throughput systems, especially for applications with many transactional conflicts, such as microservices and cloud computing. Therefore, 2PC has a performance bottleneck for distributed transaction control across multiple microservices. In this paper, we propose 2PC*, a novel concurrency control protocol for distributed transactions that outperforms 2PC, allowing greater concurrency across multiple microservices. 2PC* can greatly reduce overhead because locks are held throughout the transaction process. Moreover, we improve the fault-tolerance mechanism of 2PC* using transaction compensation. We also implement a middleware solution for transactions in microservice support using 2PC*. We compare 2PC* to 2PC by applying both to Ctrip MSECP, and 2PC* outperforms 2PC in workloads with varying degrees of contention. When the contention becomes high, the experimental results show that 2PC* achieves at most a 3.3x improvement in throughput and a 67% reduction in latency, which proves that our scheme can easily support distributed transactions with multi-microservice modules. Finally, we embed our middleware scheme in the PaaS cloud platform and demonstrate its strong applicability to cloud computing through long-term analysis of the monitoring results in the cloud platform.

RAPID: A real time commit protocol

A write transaction intending to access data already accessed by the group of transactions in read mode may starve. The situation may even become worse with the simultaneous occurrence of the priority inversion if the priority level of the requesting write transaction is higher than that of the highest priority lock holding read transaction from the group of transactions currently holding the conflicting data in read mode. A Reads-write Avoid starvation and Priority Inversion period Declined (RAPID) commit protocol has been proposed that efficiently resolves the above problem by declining the priority inversion duration optimistically through lessening the number of reader transactions either requested or currently holding the conflicting data item in read mode by aborting some of them (if possible). The simulation results show that the RAPID outperforms 2PC and PIC protocol.

Efficient Server-Aided Secure Two-Party Computation in Heterogeneous Mobile Cloud Computing

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the first work that considers collusion between a malicious garbled circuit evaluator and a semi-honest server while ensuring privacy and correctness. Also, it can guarantee fairness when collusion does not exist. The security analysis shows that our protocol can securely compute any function f(x, y) against different types of adversaries in the malicious model. Also, the experimental performance analysis shows that this work outperforms the previous works for at least 10 times with the same security level.

LIFT- A Linear Two-Phase Commit Protocol

Two phase commit (2PC) protocol is used to coordinate transaction commitment in distributed database system. The 2PC protocol is further classified into linear and centralized versions. The linear version of 2PC i.e. L-2PC protocol runs serially whereas in the centralized version, the commit process allows every site involved in executing the transaction to prepare and commit in parallel. In past, very little efforts have been made to overcome the unfavourable message overhead, poor recovery process and some other problems associated with L-2PC protocol. This paper proposes a Linear and Fast-paced recovery centred Transaction commit (LIFT) protocol to improve the existing recovery mechanism of linear 2PC by allowing autonomy to the participating cohorts to inform the cohort-in-doubt about the distributed transaction’s state instead of always looking for coordinator’s stand. The performance results confirm that proposed protocol will be a better alternative.

An Efficient Ring-Based Metadata Management Policy for Large-Scale Distributed File Systems

The growing size of modern file system is expected to reach EB-scale. Therefore, an efficient and scalable metadata service is critical to system performance. Distributed metadata management schemes, which use multiple metadata servers (MDS's) to store metadata, provide a highly effective approach to alleviate the workload of a single server. However, it is difficult to maintain good metadata locality and load balancing among MDS's at the same time. In this paper, we propose a novel hashing scheme called AngleCut to partition metadata namespace tree and serve large-scale distributed storage systems. AngleCut first uses a locality preserving hashing (LPH) function to project the namespace tree into linear keyspace, i.e., multiple Chord-like rings. Then we design a history-based allocation strategy to adjust the workload of MDS's dynamically. Besides, we propose a two-layer metadata cache mechanism, including server-side cache and client-side cache to provide the two stage access acceleration. Last but not least, we introduce a distributed metadata processing 2PC Protocol Based on Message Queue (2PC-MQ) to ensure data consistency. In general, our scheme preserves good metadata locality as well as maintains a high load balancing between MDS's. The theoretical proof and extensive experiments on Amazon EC2 demonstrate the superiority of AngleCut over previous literature.

Cheaper Private Set Intersection via Differentially Private Leakage

Abstract In this work we demonstrate that allowing differentially private leakage can significantly improve the concrete performance of secure 2-party computation (2PC) protocols. Specifically, we focus on the private set intersection (PSI) protocol of Rindal and Rosulek (CCS 2017), which is the fastest PSI protocol with security against malicious participants. We show that if differentially private leakage is allowed, the cost of the protocol can be reduced by up to 63%, depending on the desired level of differential privacy. On the technical side, we introduce a security model for differentially-private leakage in malicious-secure 2PC. We also introduce two new and improved mechanisms for “differentially private histogram overestimates,” the main technical challenge for differentially-private PSI.

Efficient and non-blocking agreement protocols

Large scale distributed databases are designed to support commercial and cloud based applications. The minimal expectation from such systems is that they ensure consistency and reliability in case of node failures. The distributed database guarantees reliability through the use of atomic commitment protocols. Atomic commitment protocols help in ensuring that either all the changes of a transaction are applied or none of them exist. To ensure efficient commitment process, the database community has mainly used the two-phase commit (2PC) protocol. However, the 2PC protocol is blocking under multiple failures. This necessitated the development of non-blocking, three-phase commit (3PC) protocol. However, the database community is still reluctant to use the 3PC protocol, as it acts as a scalability bottleneck in the design of efficient transaction processing systems. In this work, we present EasyCommit protocol which leverages the best of both worlds (2PC and 3PC), that is non-blocking (like 3PC) and requires two phases (like 2PC). EasyCommit achieves these goals by ensuring two key observations: (i) first transmit and then commit, and (ii) message redundancy. We present the design of the EasyCommit protocol and prove that it guarantees both safety and liveness. We also present a detailed evaluation of EC protocol and show that it is nearly as efficient as the 2PC protocol. To cater the needs of geographically large scale distributed systems we also design a topology-aware agreement protocol (Geo-scale EasyCommit) that is non-blocking, safe, live and outperforms 3PC protocol.

Non‐blocking two‐phase commit using blockchain

SummaryThe two‐phase commit (2PC) protocol has long been known to have a provably inevitable vulnerability to blocking or non‐progress amidst server crashes, even when the distributed database system guarantees the most demanding timing‐related or “synchrony” requirements. Our aim here is to eliminate this vulnerability by using a blockchain for coordinating 2PC execution. We present the impossibilities, the possibilities, the cost, and the trade‐offs in this blockchain‐based approach to blocking‐free management of distributed transactions. We prove that a non‐blocking and blockchain‐coordinated 2PC protocol can exist only if both the blockchain and distributed database systems meet synchrony requirements; otherwise, although blocking remains eliminated, transactions can unnecessarily abort. We present a blockchain‐coordinated 2PC protocol and provide rigorous arguments for its correctness under the synchrony requirements. We then implement this protocol on the Ethereum Testnet and demonstrate, through our experiments, that the monetary cost of executing smart contracts is quite small, that the protocol performance slows down when using a public blockchain like Ethereum, and that even major violations of synchrony requirements lead only to relatively small increases in unnecessary aborts. We thus identify a trade‐off between improving protocol performance and admitting a risk that transactions could occasionally abort unnecessarily.

On the Power of Secure Two-Party Computation

Ishai, Kushilevitz, Ostrovsky and Sahai (STOC 2007; SIAM J Comput 39(3):1121–1152, 2009) introduced the powerful “MPC-in-the-head” technique that provided a general transformation of information-theoretic MPC protocols secure against passive adversaries to a ZK proof in a “black-box” way. In this work, we extend this technique and provide a generic transformation of any semi-honest secure two-party computation (2PC) protocol (with mild adaptive security guarantees) in the so-called oblivious-transfer hybrid model to an adaptive ZK proof for any $$\textsf {NP}$$ language, in a “black-box” way assuming only one-way functions. Our basic construction based on Goldreich–Micali–Wigderson’s 2PC protocol yields an adaptive ZK proof with communication complexity proportional to quadratic in the size of the circuit implementing the $$\textsf {NP}$$ relation. Previously such proofs relied on an expensive Karp reduction of the $$\textsf {NP}$$ language to Graph Hamiltonicity [Lindell and Zarosim (TCC 2009; J Cryptol 24(4):761–799, 2011)]. As an application of our techniques, we show how to obtain a ZK proof with an “input-delayed” property for any $$\textsf {NP}$$ language without relying on expensive Karp reductions that is black box in the underlying one-way function. Namely, the input-delayed property allows the honest prover’s algorithm to receive the actual statement to be proved only in the final round. We further generalize this to obtain a “commit-and-prove” protocol with the same property where the prover commits to a witness w in the second message and proves a statement x regarding the witness w in zero-knowledge where the statement is determined only in the last round. This improves a previous construction of Lapidot and Shamir (Crypto 1990) that was designed specifically for the Graph Hamiltonicity problem and relied on the underlying primitives in a non-black-box way. Additionally, we provide a general transformation to construct a randomized encoding of a function f from any 2PC protocol that securely computes a related functionality (in a black-box way) from one-way functions. We show that if the 2PC protocol has mild adaptive security guarantees (which are satisfied by both the Yao’s and GMW’s protocol), then the resulting randomized encoding can be decomposed to an offline/online encoding.

Security and Efficiency Enhanced Revocable Access Control for Fog-Based Smart Grid System

With the popularity of smart grids, plentiful of smart devices have been put into use, such as smart meters and power assets. Due to limited computation capabilities and storage spaces of these devices, the collected data need to be “outsourced” towards the data server for processing and storage. The data owners, therefore, lose direct control over these “outsourced” data, leading to significant security issues of the users’ data. In this paper, aiming at solving this problem, we propose a multi-authority Ciphertext Policy Attribute-based Encryption (CP-ABE) scheme with revocation for the fog-based smart grid system. Specifically, in order to achieve attribute revocation without requiring users to be always online, we use the DH (Diffie-Hellman) tree to distribute the group key statelessly, which also solves the problem of collusion attack initiated by revoked user and valid user. To improve security of our proposed scheme, we remove the trusted key authority (KA) by using a secure two-party computation (2PC) protocol between the KA and the cloud service provider to generate user private key. To improve efficiency of our proposed scheme, we combine user and attribute revocation, and outsource complex calculations to fog nodes. Furthermore, our proposed scheme uses attribute group key and leaf private key together to protect user proxy key, which reduces the storage overhead of the system and improves the security. Both security analysis and experimental results demonstrate that our proposed scheme can balance the security objectives with the efficiency.

An Efficient 2-Party Private Function Evaluation Protocol Based on Half Gates

Private function evaluation (PFE) is a special case of secure multi-party computation (MPC), where the function to be computed is known by only one party. PFE is useful in several real-life applications where an algorithm or a function itself needs to remain secret for reasons such as protecting intellectual property or security classification level. In this paper, we focus on improving 2-party PFE based on symmetric cryptographic primitives. In this respect, we look back at the seminal PFE framework presented by Mohassel and Sadeghian at Eurocrypt’13. We show how to adapt and utilize the well-known half gates garbling technique (Zahur et al., Eurocrypt’15) to their constant-round 2-party PFE scheme. Compared to their scheme, our resulting optimization significantly improves the efficiency of both the underlying Oblivious Evaluation of Extended Permutation (OEP) and secure 2-party computation (2PC) protocols, and yields a more than 40% reduction in overall communication cost (the computation time is also slightly decreased and the number of rounds remains unchanged).

An Efficient Scheme for Facilitating Secure Data Sharing in Decentralized Disruption Tolerant Networks

Wireless Sensor Nodes in military situations, for example, a war zone or an unfriendly locale are liable to experience the ill effects of discontinuous system network. Objective: To design an efficient data recovery scheme for Decentralized DTNs in military environments, based on CP-ABE methodology, making Security the utmost prioritized characteristic. Analysis: Latest Innovations in Disruption Tolerant Networks (DTN) are getting to be effective arrangements, that permit mobile devices carried by military personnel to correspond with one another and access the classified data or to command effectively by manipulating the independent-autonomous repository nodes. Out of these, the most difficult issues in the situation are the authorization strategies and those approaches' improvisation for secure data recovery. Methodology: Cipher text Policy-Attribute Based Encryption (CP-ABE) is a promising cryptographic technique to address the access authority issues. The issue of applying CP-ABE in decentralized DTNs presents a few security and protection challenges like attribute revoke, key security, and regulation of attributes issued from distinct authorities. So, we implement CP-ABE along with 2PC (Two Phase Commit) protocol and where numerous key authorities deal with their attributes autonomously to overcome the above mentioned challenges. Findings/Improvements: We implement the proposed System as a stand-alone application in Eclipse IDE. We've been successful in implementing this system to achieve the security requirements we've been discussing at length in the paper and we've demonstrated how to recover the data in very hostile military environments in an efficient and a secured manner.