The rapid development of the automotive industry and the Internet of Vehicles (IoV) has led to an exponential growth of distributed vehicular data, driving the need for secure and efficient collaborative machine learning solutions. However, existing distributed collaborative machine learning (DCML) approaches, such as federated learning and split learning, face significant challenges in IoV scenarios, including limited training efficiency, centralized aggregation vulnerabilities, and constrained privacy and model protection. To address these issues, we propose a blockchain-based split federated learning (BSFL) scheme for IoV applications. BSFL non-trivially combines federated learning and split learning to enable vehicles with low computational power to participate in parallel training, improving both model accuracy and training efficiency. By utilizing blockchain as a decentralized infrastructure, BSFL eliminates the risks of single points of failure and ensures model consistency through Byzantine fault-tolerant consensus. Furthermore, we design a noise addition mechanism based on differential privacy to safeguard client data privacy and model security. Formal security analysis and extensive experiments demonstrate that BSFL achieves enhanced privacy, security, and training performance. Comparing to related DCML schemes, BSFL reduces computational overhead by up to 88.84% and client training time by up to 29.49% while maintaining comparable accuracy. When training on ResNet-50 based on CIFAR10, BSFL achieved an accuracy of 93.15%. And the verification process for each model’s training results on the blockchain requires 1.49 ms.

Byzantine Fault Tolerance (BFT) protocols are a critical research area in distributed systems and blockchain consensus due to their capacity to deliver high throughput and low latency. Traditional BFT protocols typically rely on a single leader to propose transactions and aggregate votes, which often creates a bottleneck due to the leader’s limited communication and computational capacities. The introduction of multi-leader BFT has the potential to mitigate this issue by increasing system parallelism. However, existing approaches fail to address the challenge of electing multiple leaders and lack a comprehensive analysis of the relationship between the number of leaders, security constraints, and system throughput. In this paper, we study the performance and security of multi-leader BFT protocols. Initially, we introduce a secret multi-leader election method resistant to corruption attacks where selected leaders’ identities remain unknown to others until they proposes transactions. Then, we present specific multi-leader BFT constructions that support a pipelined processing methods, realizing high processing parallelism and optimized throughput. Besides, a cross-leader view-change mechanism is designed for multi-leader BFT to enable efficient replacement of malicious leaders. Furthermore, we analyze the impact of the number of leaders on security and demonstrate that our proposals meet the required security standards. Experimental results reveal that the system achieves a throughput of up to 101 ktx/sec with 128 nodes, highlighting the potential of multi-leader BFT to significantly enhance the performance of blockchain systems.

Federated Learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, it faces critical security challenges, including centralization risks and poisoning attacks, which degrade robustness and scalability. Existing schemes struggle to simultaneously mitigate targeted and untargeted poisoning attacks, impose restrictive adversary ratio assumptions (poison ratio < 50%), and suffer from privacy-performance trade-offs. To address these limitations, we propose SharBipole, a decentralized FL scheme integrating sharding blockchain with a novel dual-metric defense mechanism, Bipole. SharBipole employs a Byzantine Fault Tolerant-enabled sharding architecture to eliminate single points of failure, reduce communication overhead, and enable parallel model aggregation. Meanwhile, the Bipole module defends against poisoning attacks using two adaptive similarity metrics to filter malicious updates dynamically. Reinforcement learning optimizes threshold adjustments, while noise-aware adaptive clipping balances privacy and model utility. Further, we give convergence analysis to prove the theoretical soundness and scalability of SharBipole. Lastly, extensive experimental evaluations demonstrate that SharBipole supports poison ratios exceeding 50% and improves throughput and latency. The model replacement attack with 60% adversaries is entirely ineffective against SharBipole, and the label-flipping attack achieves an attack success rate of only 2.344%. SharBipole establishes a scalable, secure, and privacy-preserving solution for distributed learning in massive environments.

Imagine a group of users wishing to perform offline micropayments (e.g., splitting the daily dinner bills) without revealing who paid what or relying on a central coordinator. Existing blockchain Payment Channel Networks (PCNs) fall short in such scenarios: they are designed for pairwise payments instead of multi-party settings, leak transaction metadata, and struggle to ensure consistent channel state under adversarial conditions. We present MystChannel, a privacy-preserving blockchain multi-party payment channel protocol. MystChannel introduces Blind Ring Adaptor Signatures (BRAS) to achieve transaction unlinkability and value privacy, independent of fixed payment amounts. To further ensure robustness against malicious par ticipants, we integrate an aggregable multi-signature scheme with a pipelined multi-leader Byzantine Fault Tolerant consensus protocol, enabling concurrent transaction processing and con sistent state agreement. MystChannel is provably secure under the Universal Composability (UC) framework. We implement and evaluate MystChannel, demonstrating that it achieves a 59.77× speedup over Bitcoin on-chain payments and is 77.375× faster than the state-of-the-art privacy-preserving multi-party channel AMHL [NDSS’19]. Our results affirm MystChannel as an efficient and secure off-chain payment infrastructure.

In Web 3.0's pursuit of a decentralized and user-autonomous network, traditional access control methods, such as central servers and weak decentralized algorithms, are insufficient regarding security, fault tolerance ability, and scalability. To solve this, we first design a decentralized multi-committee attribute-based encryption, X-ABE, to address the weak decentralization and low fault tolerance in Multi-Authority Attribute-Based Encryption (MA-ABE). X-ABE replaces MA-ABE's fragile attribute authorities with robust attribute committees, each composed of multiple nodes. By developing dual-wrapped shares techniques, we address the increased dimensionality challenge of secret sharing while maintaining only 1 distributed key generation instance. Also, a formal security definition and proof under the partial adaptive model are given using dual system encryption. Second, X-LOCK, an X-ABE based decentralized access control utilizing consensus plus sharding, is proposed for Web 3.0, to achieve full decentralization, consistency, fault tolerance, user autonomy, and scalability. Third, X-ABE-R is proposed for attribute revocation and is demonstrated in X-LOCK-R with sharding blockchain as an immutable revocation ledger. Fourth, a formal definition and comparative analysis of X-ABE's fault tolerance abilities are demonstrated, covering aspects of liveness and safety, along with the complexity analysis. Fifth, practical evaluations are conducted, demonstrating that while improving fault tolerance, the overhead remains acceptable.

With the approach of the quantum era, public key cryptography (PKC) faces risks, which also presents challenges to blockchain technologies that utilize PKC as a core component. Sharding blockchain is a promising way to realize scalability, yet current research does not consider quantum-resistant sharding blockchains as it is non-trivial to design cross-shard communication and transaction processing method without PKC. Besides, blockchain enables reliability in data transmission and unbreakable communication while current schemes suffer from high overhead and low throughput. In this paper, we propose a quantum-resistant sharding blockchain (QRShar) and a secure data transmission scheme (QRDT) to fill the above gap. Firstly, we design a secure and efficient cross-shard communication pattern utilizing hash-based message authentication code (HMAC) and erasure code to reduce the transmission load and achieve high efficiency. Secondly, we propose the a quantum-resistant sharding blockchain utilizing optimized cross-shard transaction processing method to decrease the consensus execution frequency. Thirdly, we introduce a quantum-resistant key agreement protocol through the verifiable secret sharing on cryptographic hash function and we also offer a data transmission scheme to realize efficient QRDT. Furthermore, we conduct security analysis and performance evaluations for our schemes. The results show that the QRShar throughput can reach up to 34 KTPS and the latency stays below 2 seconds. The key agreement latency is just 43ms.

Query rewriters transform a query into a more efficient yet semantically equivalent form, which is vital for optimizing query execution. Despite its importance, query rewriting is inherently complex, influenced by factors including rewrite rule design, rule interactions, and semantic preservation. Consequently, its implementation struggles to prevent problems, which may result in system crashes or incorrect query results. Existing DBMS testing approaches are generally designed for broad bug detection. However, due to the diversity of rewrite rules, they cover only a limited subset of rewrite scenarios, potentially overlooking critical bugs.In this paper, we propose Abstract Rule Guided (ARG) fuzzing to detect bugs in query rewrites. The key idea is to use feedback from abstract rules to guide query generation, thereby activating more rewriting logic and enhancing bug detection. Abstract rules provide a unified representation of the patterns (e.g., AST structures and related constraints) that trigger rewrites, as well as the resulting transformations. We track abstract rules to identify which patterns have been covered. This feedback is then used to dynamically adjust query generation, prioritizing unexplored patterns to avoid redundancy and expose more rewriting logic. We implemented ARG to test four popular query rewrites, namely Apache Calcite, WeTune, SQLSolver, and LearnedRewrite. ARG discovered 38 previously unknown bugs, consisting of 4 crashes, 13 invalid SQL outputs, and 21 semantic deviations. Among them, 19 have been confirmed, while the remaining cases are still under investigation. We also compared ARG against popular DBMS testing tools. In 24 hours, ARG triggered 76% and 1017% more written rules, triggered 13 and 15 more bugs than SQLsmith and SQLancer, respectively.

The rapid proliferation of edge computing and the Internet of Things (IoT) has positioned blockchain technology, particularly consortium blockchains, as a critical solution for ensuring data reliability and integrity. However, network partitioning-induced delays in information synchronization can severely compromise the robustness of consortium blockchain systems. Exploiting these delays, malicious actors may coerce specific nodes into participating in multiple partitioned sub-networks simultaneously, initiating conflicting transactions and threatening the system’s overall consistency. Existing double-spending defenses, designed primarily for traditional network environments, are insufficient to address the unique challenges posed by partitioning-induced vulnerabilities. This paper introduces a Trusted Execution Environment (TEE) based defense mechanism that securely binds consensus nodes to specific hardware platforms. By restricting node participation to their designated hardware environments, this approach effectively mitigates the risk of conflicting transactions across sub-networks. Experimental results confirm the effectiveness of the proposed mechanism, demonstrating significant improvements in the consistency and security of consortium blockchain systems, particularly in defending against double-spending attacks under network partitioning scenarios.

\textit{Federated Learning} (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. \romannumeral1) It is difficult to achieving \textit{poisoning robustness} and \textit{data privacy} while ensuring high \textit{model accuracy}. Malicious clients can launch \textit{poisoning attacks} that degrade the global model. Besides, aggregators can infer private data from the gradients, causing \textit{privacy leakages}. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. \romannumeral2) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure \textit{Multi-party Computation} (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to $<50\%$ malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined \textit{Byzantine Fault-Tolerant} (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in $2$ minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.

In recent years, Metaverse has experienced rapid development and has shown significant potential in various fields. Secure access control protocols are crucial for protecting digital assets and virtual services within the Metaverse. However, existing research suffers from the issue of single points of failure, which threatens data security and privacy when facing malicious adversaries. Additionally, current solutions face scalability challenges, rendering them unsuitable for handling the massive volume of user requests in large-scale Metaverse environments. To overcome these limitations, this paper introduces Metaverse Lock (MetaL), a fully decentralized user-driven access control scheme designed for digital assets and virtual services in the Metaverse. MetaL leverages our designed multi-committee attribute-based encryption (MC-ABE) scheme with a partially synchronous communication model, which enhances the security and fault-tolerance of attribute-based encryption. Additionally, MetaL integrates sharding blockchain technology and consensus algorithms to ensure consistency and liveness within the committee and provide immutability of the key information. The expansion of the committee enhances security and fault tolerance but also introduces additional efficiency overhead. To address this, we propose an innovative double-layer sharding architecture. Experimentally, we present a proof-of-concept implementation of both MC-ABE and MetaL, demonstrating that our approach significantly improves fault tolerance while maintaining an acceptable overhead.

Blockchain technology has experienced substantial development and has found extensive applications in the Internet of Things (IoT), which facilitates decentralized communications between devices. Blockchain enables individuals to record transactions, store data, and exchange value within a distributed ledger. However, with the development of blockchain, the performance bottleneck caused by scalability issues has become increasingly prominent. The sharding technique presents an effective solution to the scalability problem of blockchain systems by partitioning a complex blockchain network into multiple smaller node clusters. Each cluster independently maintains a ledger, reducing complexity and enhancing system efficiency. Besides, payment channels allow users to interact off-chain and rely on the security of the main chain for final settlement, accelerating the processing of numerous small transactions. However, existing sharding techniques face challenges with cross-shard operations, including high conflict rates and inefficiencies in handling multiple small off-chain transactions. In this work, we propose a tree-based sharding protocol and by designing an ordering mechanism, our protocol could effectively resist front-running attacks. Furthermore, based on the proposed tree-based sharding protocol, a cross-shard virtual channel protocol is designed and implemented for high-frequency cross-shard transactions. Finally, we implement a prototype for our protocol in Tendermint, which achieves 6700+ transaction throughput and lower confirmation latency both in intrashard and cross-shard transactions with 16 shards compared to existing works, and we measure the cost of each phase of the cross-shard virtual channel protocol which takes approximately 250 ms to process 300 transactions.

This work proposes a multi-level compiler framework to transform programs with loop structures to efficient algorithms over fully homomorphic encryption (FHE). We observe that, when loops operate over ciphertexts, it becomes extremely challenging to effectively interpret the control structures within the loop and construct operator cost models for the main body of the loop. Consequently, most existing compiler frameworks have inadequate support for programs involving non-trivial loops, undermining the expressiveness of programming over FHE. To achieve both efficient and general program execution over FHE, we propose CHLOE, a new compiler framework with multi-level control-flow analysis for the effective optimization of compound repetition control structures. We observe that loops over FHE can be classified into two categories depending on whether the loop condition is encrypted, namely, the transparent loops and the oblivious loops. For transparent loops, we can directly inspect the control structures and build operator cost models to apply FHE-specific loop segmentation and vectorization in a fine-grained manner. Meanwhile, for oblivious loops, we derive closed-form expressions and static analysis techniques to reduce the number of potential loop paths and conditional branches. In the experiment, we show that CHLOE can compile programs with complex loop structures into efficient executable codes over FHE, where the performance improvement ranges from 1.5x to 54x (up to 10(5)x for programs containing oblivious loops) when compared to programs produced by the-state-of-the-art FHE compilers.

The sixth-generation (6G) network is the core technology of next-generation communication, providing high-quality, low-latency, and broad connectivity communication services for the digitalization and intelligent transformation of future society. The large-scale, low-latency access demands of 6G devices place higher requirements on authentication services. Traditional authentication and key agreement (AKA) schemes rely on a single home network (HN) server for authentication, making the system vulnerable to security and availability issues if the server experiences a crash or is attacked. Moreover, the limited performance cannot meet the authentication throughput demands of 6G. Therefore, this paper presents the first 6G distributed group authentication scheme, called 6G DGAKA. The scheme is mainly based on the Byzantine Fault Tolerance (BFT) protocol and sharding blockchain to achieve group AKA. Firstly, multiple HN servers form an HN committee, which runs BFT to handle authentication requests, ensuring consistency, liveness, and fault tolerance. Secondly, as the system user number increases, we enhance system throughput and scalability by adding more HN committees as multiple parallel shards to process authentication requests. Thirdly, we design a 6G group authentication protocol to achieve efficient batch authentication of multiple devices, while also enabling mutual authentication among devices within the group and preventing malicious behavior from group leaders. Fourthly, we conduct implementation and evaluation, demonstrating that 6G DGAKA offers excellent forward compatibility, high throughput, low latency, and scalability.

Blockchain and cryptocurrencies are developing rapidly, and the scalability issue has become a constraint on their practical application and development. Off-chain payment channel is an effective solution to the scalability problem of blockchain. Currently, various payment channel protocols have been proposed. However, privacy issues are vital in payment channels. Existing works that consider privacy issues mainly focus on payment channel networks and payment channel hubs, while there is little work on two-party and multi-party channels. In addition, many existing payment channel works that consider privacy protection fix the transaction amounts to ensure the hiding of payment relationships or rely on smart contracts, which will hinder the practical application of payment channels. In this work, we propose a two-party privacy-preserving payment channel protocol that is compatible with Bitcoin (TBPChannel), achieving value privacy and unlinkability, while supporting variable transaction amounts. On this basis, we propose a privacy-preserving multi-party payment channel protocol (MBPChannel), which removes the role of untrusted operators in previous multi-party settings and further achieves robustness. We formally model the protocols in the universal composability framework and prove the security. Finally, we implement the protocols and provide a performance evaluation. The results demonstrate the scalability and practicality of our protocols. Compared to current protocols, even though we use privacy-preserving methods, our protocols are still efficient and applicable in practice.

Federated learning enables multiple clients to collaboratively train a model without sharing their data. Secure aggregation (SA) allows for the computation of aggregated models while protecting the private models of clients from disclosure, making it highly promising in large-scale real-world applications. Masking-based SA stands out due to its higher efficiency and accuracy. However, existing masking-based SA methods face issues such as high overhead, loss of correctness under poisoning attacks, and inability to tolerate malicious participants. In this paper, we propose Aion, a robust and efficient multi-round single-mask SA tolerating malicious participants. We introduce an aggregatable SA pattern in which each client only adds a single mask and performs only one secret sharing operation, while each aggregator only reconstructs a total secret or mask. Compared to Flamingo (S & P'23), this reduces the secret sharing times from rq to q (r for training round number and q for client number per round) and lowers n aggregators' mask reconstruction overhead from O(n2q) to O(n). Furthermore, we design a lightweight evolving input validation mechanism that efficiently filters out malicious client models by dynamically updating the mask range and overall bound, thereby improving model accuracy. Besides, we present robustness enhancements that tolerate malicious clients and aggregators. These constructions support aggregator share verification and asynchronous client model utilization. Finally, experiments demonstrate that Aion outperforms Flamingo by a factor of 563.64 in speed while achieving a 97.98% reduction in message overhead with 4096 clients and 8 aggregators, effectively defending against poisoning attacks with low overhead.

The One-Tap Authentication (OTAuth) service enables users to quickly log in or sign up for app accounts using their phone number. OTAuth provides a more secure and convenient alternative to password-based and Short Message Service (SMS)-based authentication schemes. Consequently, the OTAuth service has been adopted by numerous Mobile Network Operators (MNOs) worldwide. However, a high severity vulnerability remains unaddressed in the OTAuth service, which allows an attacker to access a victim’s various app accounts, posing a significant risk to user privacy and data security. In this paper, we present LoadShow, which, to the best of our knowledge, is the first security-enhanced OTAuth scheme to address this vulnerability. We propose a novel dynamic application identification technique that aims to address the root cause of this vulnerability, i.e., the inability of MNOs to distinguish between different applications on the same device. Specifically, application identification is based on the hardware load side-channel and captures the unique CPU and GPU load characteristics of applications through the sequence of timing values of fingerprinting functions. We evaluate the effectiveness of LoadShow by accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). We also evaluate its multi-platform compatibility on devices with different architectures and models. LoadShow achieves over 90% accuracy, with a TPR exceeding 90% and an FPR below 1%. The evaluation results demonstrate LoadShow’s capability to effectively differentiate between applications on a device, defend against app impersonation attacks, and reliably identify legitimate applications.

The rapid proliferation of lightweight devices such as sensors, BLE tags, and IoT gadgets has accelerated the demand for secure and efficient location sharing across platforms. As device cooperation and data sharing become increasingly prevalent, achieving privacy-preserving cross-platform location sharing poses significant challenges. Existing solutions predominantly rely on centralized infrastructures or single platforms and expose users' queries and stored location data to inference attacks, compromising privacy and security. This paper proposes a privacy-preserving location sharing framework supporting decentralized operations across multiple independent servers. Our system enables users to privately query the location of their lightweight devices across platforms while protecting the confidentiality of queries and location reports. We leverage an LWE-based Private Information Retrieval (PIR) scheme for efficient query generation and response, ensuring strong privacy guarantees. To support dynamic device location reporting, we employ Binary Fuse Filters (BFF) for compact and efficient data insertion and retrieval. Cross-platform compatibility is achieved through standardized client-server interaction models and robust key synchronization mechanisms. Additionally, client collaboration strategies are introduced to enhance data availability and ensure reliable device localization.

Cryptocurrencies and permissionless blockchains allow nodes from all over the world to join, and their rapid development has created enormous blockchain networks with nodes spanning the globe. Blockchain network partitioning attacks split the network into separate node groups through disrupting communication, causing information inconsistency, and facilitating malicious behaviors like double-spending and selfish mining, threatening the blockchain security. Existing research primarily studies concrete partitioning attack methods. However, it is hard to analyze practical post-attack security and efficiency impacts on blockchains and design effective countermeasures. This paper studies practical network partitioning attacks’ impacts on existing proof-of-work-based (Bitcoin) and proof-of-stake-based (Ethereum) permissionless blockchains. We theoretically analyze and experimentally confirm the adverse effects of network partitioning on blockchain performance and security. Network partitioning will cause blockchain throughput to plummet, and cause block generation delay to increase rapidly. In our experiments on Ethereum 2.0, when the bandwidth between the partitioned networks is lower than 768 Kbps, the throughput begins to plummet precipitously until it ultimately falls to 0. What’s worse, network partitioning will significantly increase the success rate of double-spending. In our experiments on Bitcoin, when the bandwidth between the partitioned networks is less than 256 Kbps, the success rate of double-spending reaches 50%. To solve the above issues, we propose countermeasures leveraging a freezing threshold to safeguard the security of permissionless blockchains and resist double-spending attacks. We experimentally validate that the countermeasures enhance the resistance of permissionless blockchains to network partitioning attacks. It reduces the probability of double-spending in partitioned networks, thereby ensuring security and reliability.

In recent years, Generative Adversarial Networks (GANs) have become essential tools in artificial intelligence research. Field Programmable Gate Arrays (FPGAs) offer remarkable flexibility, high performance, and energy efficiency for deploying GANs. However, the open and reprogrammable architecture of FPGAs, despite its advantages, introduces risks of unauthorized access and reverse engineering. To address this challenge, this article presents a novel approach integrating Physical Unclonable Functions (PUFs) and logos to protect the Intellectual Property Rights (IPR) of GANs. Our method establishes a closed-loop conversion process where logos are transformed into PUF responses, generating unique identities fed into the GAN to reproduce the original logo. By embedding PUF response information into latent vectors, the generator produces images with embedded logos. Thanks to the uniqueness of PUF, a robust binding of the logo, FPGA, and GANs' IPR is implemented, allowing verification of the IPR with the assistance of a unique FPGA fingerprint, even when a publicly available logo is used. Experimental results show that embedding the logo does not change the performance of the original GANs, and the logo detection rate exceeds 90%. At the same time, the scheme can effectively resist brute force, fine-tuning and pruning attacks.

The surge in image sharing on social media platforms escalates private information extraction for commercial use, increasing user demand for privacy protection. However, the dynamics of group communication within online social networks and the image compression imposed by platforms present significant challenges to secure key exchange and reliable image sharing in existing solutions. In this paper, we propose PrivSocial to prevent social media platforms from extracting private information in images shared within group communications. Specifically, we propose two frameworks, a server-based framework and a subscription-based framework, making PrivSocial applicable to different social media platforms and providing users with optional security levels, enhancing the flexibility and efficiency. To achieve intra-group key agreement and ensure image privacy protection, both frameworks integrate optimized continuous group key agreement and a novel image encryption scheme resisting compression. We implement an Android-based Priv-raster application and deploy a prototype on Twitter. Furthermore, we evaluate the proposed encryption scheme, and experimental results show that it has efficient encryption and decryption performance while being resistant to jigsaw puzzle solver attacks. The multi-user simulation experiments also demonstrate that the processing time of a single user is mere milliseconds, and the scheme can efficiently support tens of thousands of groups.