With the growing adoption of electronic voting in digital societies, ensuring voter anonymity and ballot integrity is essential for secure and trustworthy elections. Blind ring signature is a promising cryptographic primitive that simultaneously provides blindness (concealing the link between signer and message) and ring anonymity (hiding the actual signer within a group), thus enabling anonymous yet verifiable voting. However, most existing schemes either rely on discrete logarithm assumptions, rendering them vulnerable to quantum adversaries, or fail to achieve both blindness and ring anonymity within a single construction. In the post quantum setting, only a handful of lattice-based blind ring signature schemes have been proposed, and all suffer from prohibitively large signature sizes, limiting their practicality for large-scale deployments. Consequently, there is a pressing need for lattice-based blind ring signatures that achieve post-quantum security, simultaneous blindness and ring anonymity, and practical efficiency for large-scale elections. In this work, we present LBRS, the first lattice-based blind ring signature scheme that transforms the two-round SnowBlind protocol [Crypto'23] from the discrete logarithm setting to the lattice setting while seamlessly integrating ring signature functionality. Unlike existing solutions, our design achieves post-quantum security while preserving blindness and introducing anonymity, making it robust against quantum attackers and capable of simultaneously providing blindness and ring anonymity. We implement LBRS within a complete post-quantum anonymous voting architecture, ensuring end-to-end security and full voter anonymity, while maintaining ballot integrity, enabling transparent and verifiable tallying, and supporting practical deployment. We formally prove that LBRS satisfies correctness, anonymity, blindness, and one-more unforgeability under standard lattice assumptions. Experiments show that LBRS reduces blind signature sizes to 58.37% and real signature sizes to 2.75%-16.52% of those in prior arts, while significantly decreasing registration time and maintaining comparable costs in other phases. These advantages scale with ring size, making LBRS a strong candidate for large-scale, post-quantum secure elections.

Web data management has become crucial to data sharing among users and servers. One promising approach to guaranteeing the privacy of shared data is searchable encryption (SE), which allows users to outsource encrypted data to the web server, which can then respond confidentially to keyword queries. Several SE schemes support access control to meet data-sharing requirements. However, several works (e.g., Zhang TSC'23, Zhang TCC'21) only focus on single-user access control and ignore the need for multi-user scenarios. Besides, a malicious data owner may send useless ciphertexts to the web server, potentially making the system insecure and impractical (e.g., Wang TPDS'22, Xu TDSC'20). As a result, research regarding owner authentication and multiuser access control in SE schemes remains underexplored. In this work, we construct SEOMA, the first multi-keyword encrypted search primitive supporting owner authentication and multi-user access control for Web data management. Unlike existing solutions, our design achieves owner authentication and multi-user access control simultaneously in a malicious setting. We incorporate attribute encryption to realize the attribute authentication for a data owner. Then, we leverage the policy tree and linear secret-sharing techniques to achieve hierarchical access control for users. We also formalize and demonstrate its security in a random oracle model by reducing to the DBDH and CBDH problem. Eventually, we conduct comprehensive performance evaluations compared to existing state-of-the-art schemes. Specifically, the computation and communication overhead is only 0.05-0.4× and 0.07-0.47× compared to prior arts, respectively.

Transformer inference based on large multimodal models (LMM) has been extensively adopted in ubiquitous intelligence applications due to its exceptional abilities across various domains. However, this emerging service paradigm also exposes a huge attacking surface, making it more susceptible to the risk of sensitive content and model exposure. While steady progress has been made recently in enabling secure inference, existing studies mainly focus on the inference service for singlemodal data. Moreover, it remains underexplored how to achieve an accuracy-efficiency trade-off while ensuring adaptability in secure inference service. In this work, we present SecLMM, the first verifiable service tailored for secure and lightweight LMM-based inference on resource-constrained servers. SecLMM is built from a delicate synergy of techniques from secure multi-party computation (MPC) and LMM domains. To support secure LMM-based inference, we propose to convert the activation functions into MPCfriendly functions by analyzing the derivative, eliminating the need for retraining. To address multimodal data heterogeneity, we explore a secure vectorization scheme to process multimodal data. Besides, we adopt an effective lightweight scheme to remove redundant weights, effectively reducing deployment overhead. Moreover, we integrate a validation scheme based on multimodal mixed datasets to further enhance the reliability of the inference service. Evaluations show that SecLMM can drive full-content protection while maintaining LMM-based inference accuracy comparable to the plaintext domain.

Encrypted databases have been extensively studied with the increasing concern of data privacy in cloud services. For practical efficiency, most encrypted database systems are built under Dynamic Searchable Symmetric Encryption (DSSE) schemes to support fast query and update over encrypted data. However, DSSE schemes allow leakages in their security frameworks, especially access-pattern leakages (i.e., the search results corresponding to queried keywords), which lead to various attacks to infer sensitive information of queries and databases. Existing oblivious-access techniques, such as Oblivious RAM and differential privacy, suffer from excessive communication overhead and loss of query accuracy. In this paper, we propose a new DSSE scheme that enables access-pattern hiding keyword search and update operations. Servers can obliviously query and update databases within only a single communication round. Our building block is based on the Distributed Point Function (DPF), an advanced secret sharing technique that provides provable security guarantees against adversaries with arbitrary background knowledge. Moreover, we devise a novel update protocol that integrates DPF and Somewhat Homomorphic Encryption (SHE) such that servers can obliviously update their local data. We formally analyze the security and implement the prototype. The comprehensive experimental results demonstrate the security and efficiency of our scheme.

Public key authenticated encryption with keyword search (PAEKS) has been widely studied in cloud storage systems, which allows the cloud server to search encrypted data while safeguarding against insider keyword guessing attacks (IKGAs). Most PAEKS schemes are based on the discrete logarithm (DL) hardness. However, this assumption becomes insecure when it comes to quantum attacks. To address this concern, there have been studies on post-quantum PAEKS based on lattice. But to our best knowledge, current lattice-based PAEKS exhibit limited applicability and security, such as only supporting single user scenarios, or encountering secret key leakage problem. In this paper, we propose FS-MUAEKS, the forward-secure multi-user authenticated searchable encryption, mitigating the secret key exposure problem and further supporting multi-user scenarios in a quantum setting. Additionally, we formalize the security models of FS-MUAEKS and prove its security in the random oracle model (ROM). Ultimately, the comprehensive performance evaluation indicates that our scheme is computationally efficient and surpasses other state-of-the-art PAEKS schemes. The ciphertext generation overhead of our scheme is only 0.27 times of others in the best case. The communication overhead of our FS-MUAEKS algorithm is constant at 1.75MB under different security parameter settings.

The natural forking severely compromises the security and wastes resources of Blockchain. Current analyses of the natural forking are carried out from microscale and macroscale perspectives, each facing challenges in generality and accuracy respectively. This results in dire straits that the forking arising from the network layer cannot be solved within the same layer, and existing defense schemes concentrate on the consensus layer, unfortunately coming at the expense of diminishing decentralized trust. Hence, to overcome these shortcomings, we propose the first reconnaissance-strike solution to the natural forking where the issue is recognized at the network layer and further struck on-site. Specifically, our endeavors encompass 1) analyzing the spatial-temporal transmission dynamics of the main chain. We exert mesoscale perspective by transforming the behavioral analysis of the transmitters (i.e., the nodes) into the movement analysis of the transmitted object (i.e., the main chain) which mitigates following Lévy mobility. Based on this, we quantify the dynamics of long-range leaping and short-range diffusion of the main chain transmission; 2) proposing a cost-effective anti-forking mechanism. This mechanism combats the forking with low cost by configuring logical connections at the network management level, based on the quantitative relationship between Blockchain network topology and the natural forking rate we have derived. Both theoretical analysis and extensive experiments verify that our scheme can maintain the natural forking rate not more than the given threshold in most cases.

Sample alignment is recognized as a vital component of vertical federated learning, which facilitates the integration of differential samples and high-quality model training. In this trend, providing Private Sample Alignment (PSA) among multi-clients becomes naturally necessary for preventing unauthorized sample access and client privacy exposure. However, exiting PSA protocols mainly focus on two-party scenarios and cannot be directly adapted to the multi-client delegated computing scenarios required for vertical federated learning. Besides, these studies fail to address the need for protocol robustness in practical federated Learning network environments. Therefore, we aim to design an efficient and reliable PSA protocol in multi-client vertical federated learning. In this work, we present the first practical PSA protocol for vertical federated learning, allowing multi-clients to efficiently identify common samples without revealing additional information. Toward this direction, our PSA protocol first explores the Learning With Errors (LWE) problem to create a lightweight delegated Private Set Intersection (PSI) scheme, enabling efficient sample intersection among multiple clients. To achieve the reliability of the PSA protocol, we devise a multi-client vector aggregation algorithm that securely delegates the server to calculate the sample intersection. Building on this foundation, we develop an efficient Threshold-based Private Sample Alignment (T-PSA) protocol that allows multiple clients to determine the intersection of their input samples only if the intersection size surpasses a specific threshold. We implement a prototype and conduct a thorough security analysis. Comprehensive evaluation results confirm the efficiency and practicality of our design.

The Internet of Medical Things (IoMT) has gained significant research focus in both academic and medical institutions. Nevertheless, the sensitive data involved in IoMT raises concerns regarding user validation and data privacy. To address these concerns, certificateless signcryption (CLSC) has emerged as a promising solution, offering authenticity, confidentiality, and unforgeability. Unfortunately, most existing CLSC schemes are impractical for IoMT due to their heavy computational and storage requirements. Additionally, these schemes are vulnerable to quantum computing attacks. Therefore, research focusing on designing an efficient post-quantum CLSC scheme is still far-reaching. In this work, we propose PQ-CLSCL, a novel post-quantum CLSC scheme with linkability for IoMT. Our proposed design facilitates secure transmission of medical data between physicians and patients, effectively validating user legitimacy and minimizing the risk of private information leakage. To achieve this, we leverage lattice sampling algorithms and hash functions to generate the partial secret key, then employ the sign-then-encrypt method and design a link label. We also formalize and prove the security of our design, including indistinguishability against chosen-ciphertext attacks (IND-CCA2), existential unforgeability against chosen-message attacks (EU-CMA), and linkability. Finally, through comprehensive performance evaluation, our computation overhead is just 5% of other existing schemes. The evaluation results demonstrate that our solution is practical and efficient.

Game theory is an effective analytical tool for crowdsourcing. Existing studies based on it share a commonality: the influence of players’ decisions isbilateral. However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player canunilaterallycontrol the opponent's expected payoff. Thereby, crowdsourcing games trigger conclusions that differ from traditional ones. By addressing three questions, this paper is the first work to analyze the turbulence in crowdsourcing caused by the inequality between the requestor and the worker in the ZD game. The first question reveals the potential for the requestor to exploit the worker; the second question quantifies the worker's tolerance towards exploitation, providing a basis for confrontation; the third question serves as the cornerstone for maintaining the crowdsourcing, regulating the requestor's exploitative behavior. To answer these questions, we extend ZD strategies from binary games to continuous ones, not only revealing the requestor's dominance but also enriching the theoretical system of ZD strategies and broadening their application. Furthermore, we introduce the worker's dissatisfaction degree, identifying the exponential trend and decay rate, revealing optimal timing and speed for the worker's effective confrontation and maximum exploitation for the requestor. Numerical simulations have validated the effectiveness of our analyses.

Trust plays a crucial role in crowdsourcing Internet of Things (IoT), as it can be used to select trustworthy participants to improve the quality of crowdsourced services and strengthen system security. While traditional research has focused on micro-level aspects, including trust computation and propagation, a comprehensive macro-level trust analysis remains underexplored. In this paper, we propose a macroscopic trust ecosystem analysis framework for crowdsourcing IoT services. We first construct a Trust Ecosystem Model (TEM), where trust clusters serve as an abstraction to capture and quantify overall trust characteristics based on their size and structure. To analyze the dynamic evolution of TEM, we propose a Percolation-based Trust Ecosystem Analysis Model (P-TEAM), which maps the formation of trust clusters to a joint site-bond percolation process. Thus, the study of TEM evolution can be reframed into an investigation of how trust clusters evolve as users' trust attributes change. Through P-TEAM, we identify the critical thresholds associated with trust attributes that trigger trust phase transitions in crowdsourcing IoT services, which act as key metrics for evaluating the ecosystem's robustness macroscopically. Finally, we further evaluate the trust ecosystem beyond these thresholds by calculating the proportions of trusted giant components. We validate our approach on directed networks, using both synthetic and real-world datasets. The experimental results further substantiate our findings and provide valuable insights into constructing a healthy and sustainable trust ecosystem for crowdsourcing IoT services. Codes are available athttps://github.com/sd-sclab/MacroTrust.

With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.

Multimodal deep learning technologies have advanced significantly, which brings extensive applications in diverse fields. The substantial computational demands of training and prediction in multimodal deep learning have made the End-Edge-Cloud (EEC) framework popular. It is essential to protect multimodal data and model privacy in such a framework. However, traditional cryptographic methods, though secure for data and models at edge nodes, cause efficiency limitations. In this paper, we propose SecMdp, an SGX-assisted secure computational framework for multimodal data in the EEC architecture. Edge nodes are equipped with the trusted execution environment (e.g., Intel SGX) to run multimodal algorithms. Additionally, to address the side-channel attacks of SGX, we present an enhanced PathORAM algorithm, MM_PathORAM, for the multimodal training and prediction processes, which are tailored for multimodal deep learning scenarios. It accelerates multimodal data access while protecting data privacy and model security. Experimental evaluation supports the effectiveness of our design in preserving edge computing efficiency. It demonstrates negligible impact on the speed of multimodal data loading, the configuration of model parameters during training, or the accuracy of predictions.

Federated learning has emerged as a promising paradigm for large-scale collaborative training tasks, harnessing diverse local datasets from different clients to jointly train global models. In real-world implementations, client data could have label noise, causing the quality of the global model to be influenced. Existing label-correction solutions assume all the clients are discreet and fail to consider detecting the malicious clients, thus are not practical or privacy-preserving. In this paper, we present zkCor, an efficient and reliable label noise correction scheme with zero-knowledge confidentiality. Our method is designed upon FedCorr [1], but with more relaxed security assumptions. zkCor is established from the ingenious synergy of the label noise correction protocol and the zero-knowledge proof (ZKP), requiring each client to provide a computation integrity proof to the aggregator in each iteration. Thus, clients are forced to jointly guarantee label-correction reliability. We further devise a batch ZKP that is efficient and more suitable for federated learning settings. We rigorously illustrate the building blocks of zkCor and complete the prototype implementation. The extensive experiments demonstrate that zkCor can gain at least 2 to 30 times better performance than the baseline approach on verification workloads with nearly no extra proof time cost from clients.

Multi-modal learning leverages data from diverse perceptual media to obtain enriched representations, thereby empowering machine learning models to complete more complex tasks. However, recent research results indicate that multi-modal learning still suffers from " modality imbalance '': Certain modalities' contributions are suppressed by dominant ones, consequently constraining the overall performance enhancement of multimodal learning. To tackle this issue, current approaches attempt to mitigate modality competition in various ways, but their effectiveness is still limited. To this end, we propose an Euler Representation Learning-based Modality Rebalance (ERL-MR) strategy, which reshapes the underlying competitive relationships between modalities into mutually reinforcing win-win situations while maintaining stable feature optimization directions. Specifically, ERL-MR employs Euler's formula to map original features to complex space, constructing cooperatively enhanced non-redundant features for each modality, which helps reverse the situation of modality competition. Moreover, to counteract the performance degradation resulting from optimization drift among modalities, we propose a Multi-Modal Constrained (MMC) loss based on cosine similarity of complex feature phase and cross-entropy loss of individual modalities, guiding the optimization direction of the fusion network. Extensive experiments conducted on four multi-modal multimedia datasets and two task-specific multi-modal multimedia datasets demonstrate the superiority of our ERL-MR strategy over state-of-the-art baselines, achieving modality rebalancing and further performance improvements.

IOTA has emerged as a promising blockchain platform specially designed for the Internet of Things (IoT). Its distributed ledger, called tangle, adopts a directed acyclic graph (DAG) structure to achieve fast transaction confirmation and high scalability. While the tangle tremendously mitigates blockchain performance concerns relative to a traditional single chain, it simultaneously increases the potential risk of double-spending attacks. Utilizing constructing illegal tangle branches to substitute for legitimate ones, attackers inside IOTA can launch double-spending attacks and seriously compromise the tangle security. In this work, we take the first step toward investigating the problem of tangle vulnerability by leveraging the large deviation theory. The proposed scheme, called SecTangle, can assist IOTA in effectively reducing the tangle vulnerability to resist double-spending attacks. The core idea is to explore the security threshold defined and deduced to affect the robustness of the tangle by evaluating the probability of tangle vulnerability. By adjusting the critical factors of the security threshold, fake tangle branches can be found by IOTA efficiently to prevent double-spending attacks. Besides, we further devise a transaction recovery algorithm to recover time-sensitive legitimate transaction branches. This paper validates that the proposed scheme is efficient with comprehensive theoretical analysis and simulation experiments.

Key-value store is adopted by many applications due to its high performance in processing big data workloads. With the increasing concern for privacy, some privacy-preserving key-value storage systems have been proposed. A remarkable solution is to group key-value pairs into packs and then compress and encrypt each pack separately. The selection of pack size is important for key-value storage systems because it affects both the storage cost in the cloud and the bandwidth cost for data retrieval. However, existing data packing strategies do not consider the trade-off between them. In this paper, we study the optimal compression problem for encrypted key-value stores, aiming to minimize the overall cost of data outsourcing. To solve this problem, we devise an optimal pack size computation scheme, which considers both storage and bandwidth costs. Then, we propose a privacy-preserving key-value storage system. It balances the impact caused by encryption and compression without compromising system performance. Meanwhile, it supports dynamic updates and rich types of queries. Finally, we formally analyze the security of our design. Performance evaluations demonstrate that our proposed pack size computation scheme can minimize the overall cost of data outsourcing, and the designed key-value storage system is feasible in practice.

Ring signatures have been extensively researched for Cloud-assisted Electronic Medical Records (EMRs) sharing, aiming to address the challenge of “medical information silos” while safeguarding the privacy of patients’ personal information and the security of EMRs. However, most existing EMRs sharing systems that utilize ring signatures are vulnerable to quantum attacks, posing a severe challenge for the e-health scenario. To alleviate this issue, some studies have been conducted on lattice-based ring signatures. Nevertheless, there still exist two challenges. Firstly, current schemes fail to verify if multiple EMRs come from the same signer, undermining e-health reliability. Additionally, adversaries can exploit weaknesses in the network security of signers’ secret keys to forge signatures. In this paper, we propose an efficient lattice-based linkable ring signature (LLRS) for EMRs sharing to ensure patient privacy through anonymity, EMRs security through unforgeability, and checking the linkability for multiple signatures. We then present an enhancement scheme, called FS-LLRS, to additionally offer forward security, ensuring the security of previous ring signatures even if the current key has been compromised. To achieve this, we introduce a binary tree structure to divide time periods and leverage lattice basis algorithms for one-way secret key evolution, allowing users to update the secret keys periodically. Ultimately, we conduct a rigorous security analysis and compare our primitives with prior arts. In computational cost, the best performance of our LLRS and FS-LLRS schemes are just 0.17 and 0.34 times compared to others, respectively. Our LLRS scheme only incurs 0.08 times the communication overhead of others.

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service (MLaaS) has been extensively studied with increasing concerns about data privacy. It requires MLaaS providers should delete training data upon user requests. Unfortunately, none of the existing studies can efficiently achieve machine unlearning validation while preserving the retraining efficiency and the service quality after data deletion. Besides, how to craft the validation scheme to prevent providers from spoofing validation by forging proofs remains under-explored. In this paper, we introduce a backdoor-assisted validation scheme for machine unlearning. The proposed design is built from the ingenious combination of backdoor triggers and incremental learning to assist users in verifying proofs of machine unlearning without compromising performance and service quality. We propose to embed invisible markers based on backdoor triggers into privacy-sensitive data to prevent MLaaS providers from distinguishing poisoned data for validation spoofing. Users can use prediction results to determine whether providers comply with data deletion requests. Besides, we incorporate our validation scheme into an efficient incremental learning approach via our index structure to further facilitate the performance of retraining after data deletion. Evaluation results on real-world datasets confirm the efficiency and effectiveness of our proposed verifiable machine unlearning scheme.

Blockchain-based data storage has become an emerging paradigm, providing a fair and transparent data platform for decentralized applications. However, how to achieve secure on-chain verification for arbitrary SQL queries in such a decentralized storage remains under-explored. Due to the limitations of authenticated data structure (ADS), existing works either do not consider arbitrary query verification issue or fail to achieve practical gas consumption efficiency. In this paper, we present a novel arbitrary query verification scheme for decentralized storage. The proposed scheme, named$\mathsf {zkQuery}$, enables efficient public verification for arbitrary queries with zero-knowledge confidentiality.$\mathsf {zkQuery}$is built from the ingenious synergy of techniques from both zero-knowledge proof and smart contract technology. The core idea is to delegate smart contracts to fairly execute results verification and utilize our tailored zero-knowledge proof protocol to facilitate arbitrary computation in a privacy-preserving manner. The verification protocols of$\mathsf {zkQuery}$are highly customized for decentralized storage, where the complexity of on-chain verification can be completed in logarithmic time, significantly decreasing gas consumption. We rigorously provide security analysis and complete the prototype implementation. The extensive experiments over the NEAR blockchain show that$\mathsf {zkQuery}$can gain at least$2\times $better performance than the baseline approach on all metrics.

With the increasing concern about data privacy and data explosion, some encrypted and compressed key-value (KV) stores have been proposed. A remarkable way to combine encryption and compression is to pack KV pairs into packs, and then compress and encrypt each pack separately. Recent research has shown that even if the data is encrypted, adversaries can still use the leaked information about data length and access frequency to launch pattern-analysis attacks. For this problem, some schemes have been proposed to protect the length and frequency distribution of packs. However, existing solutions protect such information at the cost of high storage and bandwidth overhead. In this paper, we propose an encrypted and compressed KV store with pattern-analysis security, which can resist pattern-analysis attacks with minimal overhead. We first devise a secure KV pair packing scheme, which guarantees pack length security with bounded storage overhead. Then we propose a$K$-indistinguishable pack frequency smoothing scheme. It can protect the distribution of pack frequency with minimal bandwidth overhead. We formally analyze the security of our design and implement our proposed secure KV storage system on Redis and RocksDB. Performance evaluation results demonstrate that our design minimizes the overhead of achieving pattern-analysis security.

Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.

Pre-trained encoders in computer vision have recently received great attention from both research and industry communities. Among others, a promising paradigm is to utilize self-supervised learning (SSL) to train image encoders with massive unlabeled samples, thereby endowing encoders with the capability to embed abundant knowledge into the feature representations. Backdoor attacks on SSL disrupt the encoder's feature extraction capabilities, causing downstream classifiers to inherit backdoor behavior and leading to misclassification. Existing backdoor defense methods primarily focus on supervised learning scenarios and cannot be effectively migrated to SSL pre-trained encoders. In this article, we present a backdoor defense scheme based on self-supervised knowledge distillation. Our approach aims to eliminate backdoors while preserving the feature extraction capability using the downstream dataset. We incorporate the benefits of contrastive and non-contrastive SSL methods for knowledge distillation, ensuring differentiation between the representations of various classes and the consistency of representations within the same class. Consequently, the extraction capability of pre-trained encoders is preserved. Extensive experiments against multiple attacks demonstrate that the proposed scheme outperforms the state-of-the-art solutions.

Decentralized storage is recognized as a promising blockchain application for building trustworthy data-sharing services. Recently, privacy-preserving decentralized storage has attracted tremendous attention from the research community, due to the inherent transparent access properties of blockchain. That is, multi-users can search over encrypted data from multi-owners via untrusted storage servers. Private Set Intersection (PSI) is regarded as an ideal cryptographic scheme for this scenario because it allows multiple parties to collaboratively execute private set computations without revealing additional information. However, existing solutions do not consider the necessary verification and fault tolerance of PSI results, which is the indispensable security requirements in decentralized storage. To fill the gap, in this work, we introduce the first reliable PSI scheme for decentralized storage that provides results verifiability and fault tolerance for private set operations. Our design leverages authenticated indexing structures and Shamir's secret sharing algorithm for constructing our reliable PSI scheme with fault tolerance. To address the potential malicious behaviors of dishonest users or servers, we also propose a blockchain-assisted arbitration protocol that enables public arbitration in a privacy-preserving manner. We rigorously provide security analysis and complete the prototype implementation on Fabric. Extensive results demonstrate its practicability and feasibility for existing decentralized storage.

With the widespread development of decentralized storage, it is increasingly popular for users to store their data to the decentralized database systems for the well-understood benefits of outsourced storage. To ensure the data privacy, systems commonly require users to securely keep their private keys. Thus, the secure storage of private keys is an important issue in these systems. However, existing key-management schemes commonly rely on a Trusted Third Party (TTP), which raises critical security concerns such as the single point of failure and Distributed Denial of Service (DDoS) attacks. In this paper, we propose HasDPSS, a secure and efficient blockchain-based key-management scheme for decentralized storage systems. It uses secret sharing, a lightweight cryptographic technique, to build the decentralized key-management scheme. Considering that the reliability of managing participants has inherent heterogeneity, we introduce the hierarchical access structure to achieve fine-grained key management. Meanwhile, to adapt the node churn of decentralized key management, HasDPSS enables a dynamic management committee to provide reliable services with a proactive refresh mechanism while protecting the integrity and security of private keys. In our design, we use the dimension switch method of polynomials in the evolving process to achieve the committee change of the hierarchical access structure. The reliability of participants is guaranteed by the customized commitment protocol and the immutable property of the blockchain. We thoroughly analyze security strengths and conduct extensive experiments to demonstrate the practicality of our design.

Federated learning has recently emerged as a popular learning paradigm that enables multiple clients to jointly train a high-quality model without sharing their local training datasets. Each client will train its local model by its local dataset, and the global model will be aggregated by local models. However, training datasets from all clients are usually heterogeneous, because they are chosen by the clients themselves. This leads to over-fitting in the local models, thus affecting the performance of the global model. There are existing methods such as regularization in local optimization and improving model aggregation. However, they introduce additional computing or storage overhead.In this paper, we present a novel system design FedIR to eliminate the impact of data heterogeneity. FedIR introduces the idea of learning invariant features in domain adaptation so that the aggregated global model can handle the data heterogeneity well. We refer to the Optimal Path Search to assist model training in obtaining better invariant representations. The modifications to local model structures are very small, with little impact on local training and server aggregation. Extensive experiments demonstrate that FedIR achieves state-of-the-art performance on popular federated learning benchmarks including CIFAR-10 and CIFAR-100, with less computation cost and communication rounds.

Encrypted range query schemes that enable range-based searches over encrypted data have become an effective solution for secure data outsourcing services. However, existing schemes are still inadequate on desired functionality and security. Specifically, supporting efficient multi-range queries while hiding the data ordering leakage remains a challenging research problem. Existing works on order-hiding query schemes only work for encrypted single-range search and incur significant computational overhead due to the protection of the ordering information. In this paper, we present a privacy-preserving multi-range query scheme that can address the above problems simultaneously. It not only enables efficient multi-range queries over encrypted data but also guarantees the privacy of ordering information. To protect the ordering leakage, our design adopts an Order-hiding Encoding (OHE) scheme to support multi-range obfuscation. In addition, a novel order-hiding KD-tree index structure is designed as the core technique underlying our scheme, which accelerates efficient multi-range queries and obfuscates ordering information of encrypted values. Finally, the formal security analysis confirms that our proposed multi-range query scheme is secure in the random oracle model. The extensive experimental results conducted on real-world datasets demonstrate the practicality of our design.

Dynamic Searchable Symmetric Encryption (DSSE) is a practical cryptographic primitive that assists servers to provide search and update functionalities in the ciphertext domain. Recent work on DSSE schemes has focused on the direction of forward-privacy, requiring that newly added files cannot be linked to previously query results. However, due to the complexity of forward-privacy updates, existing schemes can only address an honest-but-curious server. It is difficult to verify updated results while preserving forward-privacy. In this paper, we explore how blockchain techniques can help us achieve a verifiable and forward-privacy DSSE scheme. Our scheme resorts to the emerging smart contract as a trusted platform to store digests for public result verification, and carefully crafts dynamic query protocols to enable encrypted search with forward-privacy. In our design, indexes are collocated with encrypted files and stored at storage-servers, which makes the blockchain light-weighted and search operations more efficient. Moreover, we propose a hybrid index design to support efficient files deletion. By using our blockchain-assisted primitive, the property collision between dynamic result verification and forward-privacy can be solved. We formally analyze the security strengths and provide the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of our blockchain-assisted DSSE scheme.

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM$^{2}$, a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient.

Member Inference Attack (MIA) is a key measure for evaluating privacy leakage in Machine Learning (ML) models, aiming to distinguish private members from non-members by training the attack model. In addition to the traditional MIA, the recently proposed Generative Adversarial Network (GAN)-based MIA can help the adversary know the distribution of the victim's private dataset, thereby significantly improving attack accuracy. For traditional attacks and this new type of attack, previous defense schemes cannot handle the trade-off between privacy and utility well. To this end, we propose a defense solution using multi-model ensemble framework. Specifically, we train multiple submodels to hide membership signals and resist MIA, achieving reduced privacy leakage while guaranteeing the effectiveness of the target model. Our security analysis shows that our scheme can provide privacy protection while preserving model utility. Experimental results on widely used datasets show that our scheme can effectively resist MIAs with negligible utility loss.

Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this paper, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.

IOTA is an emerging blockchain platform specially designed for the Internet of Things (IoT). It adopts a distributed ledger based on a directed acyclic graph (DAG) structure called the Tangle, achieving high concurrency, incremental scalability, and zero transaction fees. While the DAG-based ledger structure can promise many benefits for IOTA, it also exposes a wide attacking surface for the double-spending attack. By creating fake branches to replace the legitimate ones in the Tangle, the attackers can achieve the purpose of double-spending. Therefore, there is a great need for securing IOTA with preventive strategies. In this work, we present the design of an effective preventive strategy for IOTA that can reduce the Tangle vulnerability against the double-spending attack. Our main idea is to in-vestigate the Tangle vulnerability problem by using the large deviation theory. We provide the Tangle vulnerability probability and the decay speed of the probability. By evaluating the decay speed, we find the key factor related to the robustness level against double-spending attacks. Besides, we define and deduce the security threshold based on the evaluation results that can affect the probability decay speed of the Tangle vulnerability. With the security threshold, IOTA can efficiently inspect illegal Tangle branches to resist double-spending attacks. Finally, the comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is efficient and practical.

IOTA has emerged as a promising and feeless decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications with high-performance transaction rates and incremental scalability. To support micro-payments of IoT devices, IOTA has abandoned the original blockchain reward mechanism while IOTA nodes voluntarily contribute resources to maintain network stability. However, removing the mining rewards results in the resource cost of generating IOTA ledgers (known as the Tangle) being borne only by IOTA nodes. With the continuous expansion of the IOTA network, cost consumption is increasing. Thus, the inability to effectively reduce the cost of Tangle generation would lead to people being reluctant to dedicate resources to IOTA for maintaining the network robustness. In this paper, for the first time, we present a full-fledged transaction cost optimization scheme for IOTA, called Tangless, which can assist IOTA nodes in effectively reducing the Tangle generation cost while maintaining the strong robustness of the IOTA network. By using our proposed scheme, each IOTA node can effectively formulate the threshold of transaction approval rate in real time, maintaining the stability of the IOTA network with the optimal computational cost. We harness Lyapunov optimization theory to design a computational optimization algorithm for minimizing the total cost of nodes in IOTA. Then, we resort to large deviations theory to devise an optimized transaction rate control algorithm to further eliminate orphan Tangles that waste computational costs. Comprehensive theoretical analysis and simulation experiments confirm the effectiveness and practicability of our proposed scheme.

Fog computing has emerged as a promising framework with the rapid growth of the Internet of Things (IoT). In fog computing, the new entity, named fog device, can help the cloud process the large amount of data generated by IoT devices. Along with this trend, a location-based query scheme that collects IoT devices’ data from specific areas is an important application, especially in fog-enhanced sensor networks. However, in this application, the cloud and fog devices require the user’s query, sensors’ locations, and sensor data so that it raises critical privacy and security concerns. In this article, we devise a privacy-preserving-location-based data query scheme in fog-enhanced sensor networks, which allows the cloud and fog devices to collect sensor data from a query area without learning the three kinds of information. Specifically, we resort to a cryptographic primitive, named somewhat homomorphic encryption (SHE), with ciphertext packing to encrypt query, locations, and sensor data and efficiently calculate the distances between the user’s query and sensors. Then, we show how to build a hardware-assisted data query scheme to extract the matched data based on the distances. We formally analyze the security strengths and implement the system prototype. In order to implement secure processing within software guard extension (SGX), we make an effort to adapt the existing mathematical libraries to the advanced SGX trusted environment. Evaluation results demonstrate that our proposed design is secure and efficient.

The explosive growth of the Internet of Things (IoT) is pushing forward the paradigm of fog computing services today. An important service for most fog-enhanced applications is geographic range-match, which means the fog-nodes can accurately collect sensed data from IoT devices based on their location distances. However, due to the increasing attacking surfaces, outsourcing range query operations to untrusted fog-nodes inevitably raises new privacy concerns about query content and device location exposure. In this article, we devise a new geographic range-match scheme for fog-enhanced services, which allows fog-nodes to securely collect range-based sensed data while protecting the location privacy of IoT devices. Our main idea is to formulate the problem of encrypted geographic queries as range-based pattern matching and carefully craft security schemes to enable efficient range queries in the ciphertext domain. The proposed range-match scheme is provably secure and can reduce accessible information during distance comparisons. We formally analyze the security strengths and complete the prototype implementation. The comprehensive experimental results demonstrate the practicality of our designs.

Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.

Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.

Nowadays, many data owners choose to outsource their data to public cloud servers while allowing authorized users to retrieve them. To protect data confidentiality from an untrusted cloud, many studies on searchable encryption (SE) are proposed for privacy-preserving search over encrypted data. However, most of the existing SE schemes only focus on the single-owner model. Users need to search one-by-one among data owners to retrieve relevant results even if data are from the same cloud server, which inevitably incurs unnecessary bandwidth and computation cost to users. Thus, how to enable efficient authorized search over multi-owner datasets remains to be fully explored. In this paper, we propose a new privacy-preserving search scheme for the multi-owner and multi-user model. Our proposed scheme has two main advantages: 1) We achieve an attribute-based keyword search for multi-owner model, where users can only search datasets from specific authorized owners. 2) Each data owner can enforce its own fine-grained access policy for users while an authorized user only needs to generate one trapdoor (i.e., encrypted search keyword) to search over multi-owner encrypted data. Through rigorous security analysis and performance evaluation, we demonstrate that our scheme is secure and feasible.

With the rapid development and widespread application of crowdsourcing, the limitations of traditional systems are gradually exposed. First, traditional systems fail to protect the privacy of task requesters and workers. They typically rely on a centralized server to aggregate the task content and workers' interests, while these data contain sensitive information. Second, crowdsourcing resources in each system are isolated. The tasks in one system cannot reach potential workers in other systems. Thus, there is a great need to build a new privacy-preserving and federated crowdsourcing system. However, the existing privacy-preserving solutions rely on a trusted third party to perform key management, which is not applicable in a federated setting. To this end, we propose the first proxy-free privacy-preserving and federated crowdsourcing system. It interconnects the existing crowdsourcing systems and can perform encrypted task matching across various systems without relying on a trusted third-party authority. Our main idea is to achieve federated crowdsourcing by moving secure task matching to the trusted smart contract. To get rid of the dependence on the trusted authority, we combine the rewritable deterministic hashing technique with searchable encryption schemes to achieve secure on-chain task-matching authorization. Moreover, we utilize the puncturable encryption technique to implement secure authorization revocation. We formally analyze the security of our design and implement a prototype on Ethereum. Evaluation results demonstrate that our design is secure and efficient for blockchain-based crowdsourcing.

The increasing popularity of Online Ride-hailing (ORH) services has greatly facilitated our daily travel. It enables a rider to easily request the nearest driver through mobile devices in a short time. However, existing ORH systems require the collection of users' location information and thus raise critical privacy concerns. While several privacy-preserving solutions for ORH service have been proposed, most of existing schemes rely on an additional trusted party to compute the distance between a rider and a driver. Such a security assumption cannot fully address the privacy concerns for practical deployment. In this paper, we present a new ride-matching scheme for ORH systems, which allows privacy-preserving and effective distance calculation without involving a third-party server. Our proposed scheme enables ORH systems to securely compute the user distance while protecting the location privacy of both riders and drivers. Specifically, we resort to state-of-the-art distance calculation techniques based on Road Network Embedding (RNE), and show how to uniquely bridge cryptographic primitives like Property-preserving Hash (PPH) with RNE in depth to support privacy-preserving ride-matching services. Moreover, we also propose an optimized design to improve the matching efficiency. We formally analyze the security strengths and implement the system prototype. Evaluation results demonstrate that our design is secure and efficient for ORH systems.

Dynamic Symmetric Searchable Encryption (SSE) is a practical cryptographic primitive that enables data owners to search and update encrypted data hosted on untrusted servers. Recently, there is a growing interest to design dynamic SSE schemes with forward security. That is, the server cannot learn the association between the updated data and any query made in the past. However, due to the complexity of update operations, this security property introduces a great challenge of designing verifiable SSE schemes. It is difficult to verify the correctness of updated search results while preserving forward privacy. In this work, we explore how blockchain techniques can help us achieve a verifiable and dynamic SSE construction with forward security. First, we propose a new dynamic SSE scheme based on blockchain techniques, and apply it as the underlying building blocks to preserve forward-secure updates. Second, we resort to the emerging smart contract technique to customize a verification scheme, making updated results easily verifiable. Based on this new primitive, the robustness of the encrypted search service is ensured and forward security is preserved for update operations. Finally, we implement the prototype in Python and Solidity, and conduct performance evaluations on Ethereum. The extensive security analysis and performance evaluations on the real-world dataset demonstrate that our blockchain-assisted SSE scheme is secure and feasible.

An encrypted index is a data structure that assisting untrusted servers to provide various query functionalities in the ciphertext domain. Although traditional index designs can prevent servers from directly obtaining plaintexts, the confidentiality of outsourced data could still be compromised by observing the volume of different queries. Recent volume attacks have demonstrated the importance of sealing volume-pattern leakage. To this end, several works are made to design secure indexes with the volume-hiding property. However, prior designs only work for encrypted keyword search. Due to the unpredictable range query results, it is difficult to protect the volume-pattern leakage while achieving efficient range queries.In this paper, for the first time, we define and solve the challenging problem of volume-hiding range queries over encrypted data. Our proposed hybrid index framework, called HybrIDX, allows an untrusted server to efficiently search encrypted data based on order conditions without revealing the exact volume size. It resorts to the trusted hardware techniques to assist range query processing by moving the comparison algorithm to trusted SGX enclaves. To enable volume-hiding data retrieval, we propose to host encrypted results outside the enclave in an encrypted multimaps manner. Apart from this novel hybrid index design, we further customize a bulk refresh mechanism to enable accesspattern obfuscation. We formally analyze the security strengths and complete the prototype implementation. Evaluation results demonstrate the feasibility and practicability of our designs.

Crowdsourcing is a promising computing paradigm that utilizes collective intelligence to solve complex tasks. While it is valuable, traditional crowdsourcing systems lock computation resources inside each individual system where tasks cannot reach numerous potential workers among the other systems. Therefore, there is a great need to build a federated platform for different crowdsourcing systems to share resources. However, the security issue lies in the center of constructing the federated crowdsourcing platform. Although many studies are focusing on privacy-preserving crowdsourcing, existing solutions require a trusted third party to perform the key management, which is not applicable in our federated platform. The reason is that it is difficult for a third party to be trusted by various systems. In this paper, we present a secure crowdsourcing framework as our initial effort toward this direction, which bridges together the recent advancements of blockchain and cryptographic techniques. Our proposed design, named PFcrowd, allows different crowdsourcing systems to perform encrypted task-worker matching over the blockchain platform without involving any third-party authority. The core idea is to utilize the blockchain to assist the federated crowdsourcing by moving the task recommendation algorithm to the trusted smart contract. To avoid third-party involvement, we first leverage the re-writable deterministic hashing (RDH) technique to convert the problem of federated task-worker matching into the secure query authorization. We then devise a secure scheme based on RDH and searchable encryption (SE) to support privacy-preserving task-worker matching via the smart contract. We formally analyze the security of our proposed scheme and implement the system prototype on Ethereum. Extensive evaluations of real-world datasets demonstrate the efficiency of our design.

The explosive growth of network traffic is pushing forward the paradigm of cloud-based middlebox services today. However, due to the increasing attacking surfaces, redirecting enterprises traffic to outsourced middleboxes inevitably raises new privacy concerns about packet content exposure and unauthorized rulesets access. To address these issues, recent efforts have been made toward enabling middlebox services through encrypted traffic and middlebox rules. Following this direction, in this article, we investigate the issue of privacy-preserving header checking, which is an indispensable service of middlebox applications. Specifically, we propose two new encrypted header-matching schemes that significantly improve security and efficiency. Our main idea is to formulate the problem of encrypted header checking as range-based pattern matching, and carefully craft security designs to enable efficient header inspection in the ciphertext domain. Our first design is carefully tailored to generic range-based functions, while our second design is highly customized for contiguous rulesets to further improve the checking efficiency. We formally analyze the security strengths and implement a fully functional system prototype. The extensive experiments over the real-world rulesets demonstrate the practicality of our designs.

To accommodate massive digital data, distributed data stores have become the main solution for cloud services. Among others, key-value stores are widely adopted due to their superior performance. But with the rapid growth of cloud storage, there are growing concerns about data privacy. In this paper, we design and build EncKV, an encrypted and distributed key-value store with rich query support. First, EncKV partitions data records with secondary attributes into a set of encrypted key-value pairs to hide relations between data values. Second, EncKV uses the latest cryptographic techniques for searching on encrypted data, i.e., searchable symmetric encryption (SSE) and order-revealing encryption (ORE) to support secure exact-match and range-match queries, respectively. It further employs a framework for encrypted and distributed indexes supporting query processing in parallel. To address inference attacks on ORE, EncKV is equipped with an enhanced ORE scheme with reduced leakage. For practical considerations, EncKV also enables secure system scaling in a minimally intrusive way. We complete the prototype implementation and deploy it on Amazon Cloud. Experimental results confirm that EncKV preserves the efficiency and scalability of distributed key-value stores.

Over the past few years, enterprises start adopting software middlebox services from cloud or NFV service providers. Although this new service model is recognized to be cost-effective and scalable for traffic processing, privacy concerns arise because of traffic redirection to outsourced middleboxes. To ease these concerns, recent efforts are made to design secure middlebox services that can directly function over encrypted traffic and middlebox rules. But prior designs only work for portions of frequently-used network functions. To push forward this area, in this work, we investigate header matching based functions like firewall filtering and packet classification. To enable privacy-preserving processing on encrypted packets, we start from the latest primitive “order-revealing encryption (ORE)” for encrypted range search. In particular, we devise a new practical ORE construction tailored for network functions. The advantages include: 1) guaranteed protection of packet headers and rule specified ranges; 2) reduced accessible information during comparisons; 3) rule-aware size reduction for ORE ciphertexts. We implement a fully functional system prototype and deploy it at Microsoft Azure Cloud. Evaluation results show that our system can achieve per packet matching latency 0.53 to 15.87 millisecond over 1.6K firewall rules.

Distributed data stores have been rapidly evolving to serve the needs of large-scale applications such as online gaming and real-time targeting. In particular, distributed key-value stores have been widely adopted due to their superior performance. However, these systems do not guarantee to provide strong protection of data confidentiality, and as a result fall short of addressing serious privacy concerns raised from massive data breaches.