Federated learning (FL) is a paradigm that ensures the confidentiality and accessibility of data without requiring the collection of private data from multiple sources. It acquires an aggregation model by integrating various local models from clients. However, clients are vulnerable to numerous security and privacy threats. Existing solutions were unable to implement training models that are both dropout-resilient and lightweight while also providing verification capabilities when large-scale clients are involved in federated training. To improve the usability of FL, we propose a verifiable and lightweight multi-round secure FL framework by designing and incorporating a double-masking mechanism to ensure secure transmission. Moreover, we optimize the secure aggregation strategy by designing a dropout-resilience method via the secret-sharing mechanism. Specifically, we establish a lightweight model-secure training scheme and provide a parameter reuse strategy by constructing a full connection graph, which reduces computational cost and communication overhead. Furthermore, we propose a secure authentication protocol that enables the client to verify the accuracy of the computing results from the server. Extensive experimental evaluations indicate that our solution demonstrates relatively modest performance but superior functionality compared to current state-of-the-art methods. In particular, we can achieve the verification function with an acceptable increase in computational cost of approximately 200ms per epoch.

With the rapid development of Industrial Internet of Things (IIoT), data sharing as a cornerstone function of IIoT, has attracted considerable attention. Ciphertext-policy attribute-based encryption is extensively used to ensure confidentiality and fine-grained access control in such scenarios. Nevertheless, most existing schemes face critical challenges, including high decryption overhead, inadequate attribute privacy protection, and the absence of verifiable revocation mechanisms, which significantly impede their applications in industrial manufacturing systems. To address these challenges, this paper introduces a ciphertext-policy anonymous attribute-based encryption (CP-A$^{2}$BE) scheme with outsourced decryption and verifiable revocation. It encompasses three key innovations: Firstly, a distributed edge computing architecture is established, leveraging pre-deployed edge nodes within factories to offer outsourced decryption services. Secondly, while safeguarding the privacy of industrial data, the attribute privacy of devices and personnel is also taken into consideration. Thirdly, a verifiable revocation mechanism employing commitment-based techniques is presented to enable real-time access control updates while ensuring the data integrity of revocation operations performed by cloud servers. Experimental evaluation shows that our proposed scheme is practical for data sharing in resource-limited IIoT, especially as the decryption time consistently remains at 26 milliseconds, regardless of the number of attributes involved.

In this paper, we present$\mathtt {Antelope}$, a semi-honest large-scale secure inference system without revealing either clients’ data or model parameters. The main contributions of$\mathtt {Antelope}$are new two-party computation (2PC) protocols over a ring$\mathbb {Z}_{2^\ell }$for non-linear layers, which optimize the online computation and communication overhead thus outperforming the state-of-the-art 2PC systems. Specifically, we reformulate the comparison function as an Equality-to-Zero test followed by multiplication, decoupling the bit-wise rounding dependency in traditional secret sharing-based bit extraction. With this technique, the evaluation of the ReLU non-linear activation function is$1.7\times$-$84.5\times$faster than existing solutions in online communication cost. We also develop a suite of optimizations that improve the efficiency of secure division protocols, which are tailored to different divisor settings in the neural networks. We extend our protocols to construct efficient implementations for several building blocks such as ReLU, Maxpool, truncation, and Softmax. End-to-end evaluation on realistic ImageNet-scale networks demonstrates that$\mathtt {Antelope}$achieves over$22.3\times$and$23.0\times$online runtime speedups in LAN and WAN settings, respectively, without accuracy loss, compared to the state-of-the-art works.

Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intriguing prospects, especially for boosting machine learning on large-scale data, which are tangled with sparse data. In this paper, we investigate privacy-preserving SIP problems that have rarely been explored before. Specifically, we propose two concrete constructions, one requiring offline linear communication, which can be amortized across queries, while the other has sublinear overhead but relies on the more computationally expensive tool. Our approach exploits state-of-the-art cryptography tools including garbled Bloom filters (GBF) and Private Information Retrieval (PIR) as the cornerstone but carefully fuses them to obtain nontrivial overhead reductions. We provide formal security analysis of the proposed constructions and implement them into representative machine learning algorithms including k-nearest neighbors, naive Bayes classification, and logistic regression. Compared to existing efforts, our method achieves 2-$50\times$speedup in runtime and up to$10\times$reduction in communication.

Cloud computing, as a persuasive paradigm, offers on-demand data services. However, it faces various security threats during data sharing due to trust issues. To mitigate this problem, many cloud-based data-sharing systems employ cryptographic tools to guarantee the confidentiality of sensitive data. Nevertheless, fine-grained data sharing still suffers from many challenges, especially in complex cloud environments. In this paper, we introduce two cloud-based data-sharing systems with fine-grained access control. The first solution supports dynamic user groups, while the second solution further offers outsourced decryption, enabling compatibility with resource-constrained devices. To formalize our solution theoretically, we introduce the concept of ElGamal -type cryptosystem (ETC) and server-aided ETC with key encapsulation mechanism to generalize public-key encryption with specific features implicitly specified by ElGamal encryption. Through the application of ETC, we present generic constructions for revocable attribute-based encryption (RABE) and server-aided RABE (SR-ABE) with formal definitions and security analyses. These schemes serve as the fundamental mechanisms behind our proposed solutions. By applying the state-of-the-art attribute-based encryption scheme proposed in CCS'22, we introduce instantiations of RABE and SR-ABE with various desirable properties, including large universe, attribute multi-use, key exposure resistance, fast decryption, and more. Extensive experiments substantiate the superior performance of our proposed instantiations over previous solutions.

Conditional privacy-preserving authentication (CPPA) has been widely studied to ensure authentication, anonymity, and traceability in vehicle ad hoc networks (VANETs). Among CPPA schemes, certificateless CPPA (CL-CPPA) offers natural advantages for VANETs by avoiding the complex certificate management of PKI-based solutions and the key escrow problem of identity-based systems. However, many existing CL-CPPA schemes overlook secure message communication. When confidentiality is required, they often rely on additional encryption, which adds communication overhead comparable to that of certificate transmission in PKI-based solutions. Anamorphic signatures (proposed at CRYPTO'23) can be adopted to address this issue by embedding sensitive messages within signatures, which eliminates the need for separate encryption. However, existing anamorphic signature schemes are symmetric, requiring extensive key agreements between two entities, which is impractical for the dynamic nature of VANETs. In this paper, we introduce a new cryptographic primitive, asymmetric anamorphic signatures (AAS), which enables secure message sharing in a public-key setting, thus eliminating the need for complex key agreements. We also provide a concrete construction of AAS based on a variant of the Hohenberger-Waters signature scheme. By integrating AAS with CL-CPPA, we proposeSphinx, a scheme that ensures authentication, anonymity, traceability, and confidentiality without the overhead of additional secure transmissions or complex key agreements. Our security proofs and performance evaluations demonstrate the practicality and efficiency ofSphinx, particularly its advantage in reducing communication overhead.

Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveragingmutable samples of illegitimate cryptojackingto designsupervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures; the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively.

Using side-effect sensing information to monitor the behavior of smartphone usage raises privacy leakage concerns. However, existing research typically utilizes only a single sensing channel or performs a simple aggregation of multi-channel data to infer user behavior, without sufficiently leveraging rich spatiotemporal information embedded in the diverse sensing channels. Such a narrow focus of existing works fails to exhibit the real risk of user privacy leakage. To bridge this research gap, we propose HiddenSpy, a comprehensive study assessing the smartphone usage snooping associated with multiple sensing channels, such as accelerometers and magnetometers. We start by examining the relationship between the data gathered from each channel and daily usage behaviors, highlighting information volume differences across channels. Building on this analysis, we propose a multi-layer attention mechanism that dynamically adjusts the importance of spatiotemporal information from different channels and time frames, facilitating the efficient use of multi-channel data for behavior inference. Importantly, our work marks a pivotal shift from addressing information leakage in single channels to managing information exposure throughout the smartphone sensing system, laying the foundation for more comprehensive protective measures. To validate our approach, we collect data from forty widely-used applications and evaluate the corresponding usage behavior snooping performance. The results show that HiddenSpy improves accuracy in three common snooping tasks, while its defense mechanism reduces accuracy to a low level, effectively preventing information leakage.

Privacy concerns have been persistently afflicting individuals within online social networks (OSNs), rendering privacy-preserving communications over the Internet with authentication especially important. Unfortunately, the guarantees of privacy and authenticity are not always provided in OSNs. Individuals are still facing the challenges of being deceived or exploited. To mitigate these issues, anonymous social networks (ASNs) have emerged as a remedy for OSNs, facilitating individuals to connect with others anonymously and authentically. Despite the existence of numerous and remarkable cryptographic primitives, there are no formal solutions for ASNs except for matchmaking encryption (ME), since ME can simultaneously provide various key functionalities, i.e. bilateral access control, identity anonymity, and message authentication, to address the requirements of ASNs. In this paper, we design a system for ASNs by adopting fuzzy identity-based matchmaking encryption (fuzzy IB-ME), and the proposed scheme in this work is highly efficient. The scheme also realizes adaptive security in generic group model (GGM), which is generally adopted in pairing-based cryptography. The proposed ASNs system offers various advantages compared to the previous solutions, including 1) bilateral access control, 2) enhanced security, 3) high scalability, and 4) high efficiency. In addition to theoretical evaluations, we conduct extensive experiments to evaluate our scheme’s computational and storage efficiency. These evaluations indicate that our solution outperforms previous solutions and as well as preserves many desired functionalities.

Remote data integrity auditing enables a client to efficiently ensure the integrity of entire data stored in untrusted servers via auditing. Yet, existing solutions generally emphasize on various metrics (such as minimal storage, fast update, metadata privacy), but not audit performance (e.g., low audit time, small proof size). To this end, a label-aggregating remote data integrity auditing scheme (LARDA) was proposed in ESORICS ’22, which is the state-of-the-art work in terms of proof size and storage cost. However, LARDA needs a trusted third party (TTP) for performing data auditing for all data owners, which introduces a single point of failure since the audit process routinely needs to interact with the TTP. To address this issue, we introduce a new concept called threshold label-aggregating data auditing and propose two novel schemes. Our first solution is based on Pedersen secret sharing technique, which can significantly alleviate the key escrow problem of LARDA. Our second solution is an efficient batch verifying scheme for multiple TTP’s secret key shares, utilizing the KZG (Kate, Zaverucha and Goldberg) secret sharing technique. This scheme can maintain the size of commitment for TTP’s secret key constant rather than a linear factor with the number of TTPs. We conduct comprehensive experiments to demonstrate the scalability of our schemes. In particular, our second scheme improves the verification time for TTP’s secret key shares in constant, only requiring two pairings and one exponentiation in group with an average of 7.39 ms, regardless of the number of TTPs increasing. For our first scheme, the verification procedure requires$2t$exponentiations in group (where t is the threshold value), ranging from 2.37 ms ($t = 2$) to 26.85 ms ($t = 35$).

Smart cameras have gained immense popularity in commercial markets for their safety and security capabilities. Yet, the prevalent design of these intelligent camera systems often compels users to cede control of their data to poten-tially untrusted service providers, such as cloud services. This relinquishment can lead to unauthorized data access by these intermediaries, posing significant security and privacy risks. The conventional solutions have been to employ privacy-enhancing technologies to bypass these intermediaries, but at the cost of increased overhead for video streaming and sharing. In our study, we introduce SecSCS, a user-centric, blockchain-based secure camera system that incorporates essential features like video streaming, sharing, deletion, and permission restoration. SecSCS integrates a blockchain-enabled user login protocol with a secure device pairing mechanism that combines visual authorization with blockchain to flexibly manage the device ownership. We utilize blockchain to provide integrity protection for the video clips stored remotely, ensuring the video data remains tamper-proof. Furthermore, we present a video frame compression and a fast video encryption method aimed at boosting the efficiency of smart camera systems. Our evaluations show that, in comparison to the leading decentralized scheme, CaCTUs, SecSCS improves the computational and communication overhead for live streaming by a factor of 12.58 and 11.29, respectively, at a frame rate of 24 fps and a resolution of 720p.

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server needs to perform a process called ciphertext delegation (Sahai et al., CRYPTO’12) that periodically updates the timestamp for all ciphertexts. As the number of ciphertexts could be huge in a storage system, ciphertext delegation could pose a huge computation overhead to the server.Motivated by the popularity of commodity Trusted Execution Environment (TEE) technologies, this paper initiates the study on hardware-based revocable ABE (HR-ABE) to eliminate the (unscalable) ciphertext delegation and prevent collusion attacks between an untrusted storage server and revoked users. We formalize this new notion and present an efficient HR-ABE construction that also supports outsourced decryption for resource-constrained data users. Furthermore, HR-ABE is also designed to address the potential secret leakage problem suffered by TEE (e.g., due to side-channel attacks) so that the leakage of secrets possessed by TEE does not lead to leakage of user data. We prove HR-ABE’s security formally and benchmark its performance experimentally.

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally target only specific types of malicious perturbations and thus lack robustness. To address this issue, we present a robust defense mechanism named Shield-U, which restores traffic sign images contaminated by physical patch perturbations, providing credible data for the recognition model. In the process of implementing Shield-U, we first design a feature difference-aware perturbation generator that outputs potential sign contamination patterns. Incorporating generated perturbations during the training phase enables our restoration model to gain sufficient understanding of diverse perturbation types, thus enhancing its ability to repair various perturbed signs. Following this, we build an attention-driven restoration network to repair sign images. Finally, we evaluate the effectiveness of Shield-U using widely used sign recognition models and public datasets. The results demonstrate that our defense mechanism excels in resisting potential perturbations, increasing the average sign recognition accuracy by 50.4%.

Abstract Searchable asymmetric encryption (SAE) enables a client to search over a data owner’s encrypted data. Nevertheless, state-of-the-art SAE schemes allow a data owner to specify access control policy for a client, while they have not considered the threat case of a malicious data owner. To address the problem, this work presents a non-interactive SAE scheme with bilateral access control: (i) allowing data owner and client to both specify policies toward the other party; (ii) allowing client to perform arbitrary boolean queries with sub-linear search complexity. Technically, we extend Cash et al.’s highly scalable SSE into an asymmetric setting and introduce the property of data owner authenticity. By refining identity-based matchmaking encryption, we formalize the syntax and security definition of our SAE with identity-based bilateral access control. Moreover, the security of the proposed SAE can be reduced to discrete logistic assumption and decisional bilinear Diffie–Hellman assumption. As an enhanced extension, we present a non-interactive multi-client SAE scheme with fuzzy identity-based bilateral access control. In addition, we implement the proposed schemes in real cloud platform and evaluate their performance on a real-world dataset. The result confirms that our SAE schemes achieve bilateral access control for both data owner and client with highly acceptable efficiency.

In the digital era, one of the most significant changes in the IoT world is the popularity of digital subscriptions, where service providers upload encrypted service information to the cloud for sharing. In practice, the untrustworthy service providers may intentionally leak their private keys used to encrypt service information (for profits), allowing unauthorized subscribers to enjoy valuable service. The malicious behavior described above has become a severe obstacle to the widespread application of IoT-based digital subscriptions. To address this issue, we propose a fine-grained and sanitizable access control system (FSAC), in which service information could only be accessed by authorized subscribers. To thwart potential threats from malicious service providers, we design a sanitizable mechanism to transform the original ciphertext, ensuring that a subscriber is unable to decrypt the sanitized ciphertext solely using the leaked key of the service provider. For resource-constrained IoT devices, we further extend FSAC with outsourced decryption (FSACO) that relieves subscribers from the burden of decryption. In particular, FSACO allows subscribers to perform two exponentiation operations rather than time-consuming paring operations (as that in FSAC) to decrypt sanitized ciphertext. We conduct rigorous security analysis of our systems and demonstrate their efficient performance through extensive experiments. Specifically, the enhanced system FSACO has a minimum decryption time of approximately 0.08 ms.

In this paper, we presentVerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model.VerifyMLis generic and is immune to any obstruction by the malicious model holder during the verification process. We rely on secure two-party computation (2 PC) technology to implementVerifyML, and carefully customize a series of optimization methods to boost its performance for both linear and nonlinear layer execution. Specifically, (1)VerifyMLallows the vast majority of overhead to be performed offline, thus meeting the low latency requirements for online inference. (2) To speed up offline preparation, we first design novel homomorphic parallel computing techniques to accelerate the authenticated Beaver's triple (including matrix- vector and convolution triples) generation procedure. It achieves up to$1.7\times$computation speedup and gains at least$10.7\times$less communication overhead compared to state-of-the-art work. (3) We also present a new cryptographic protocol to evaluate the activation functions of non-linear layers, which is$4\times$–$42\times$faster and has$\gt 48\times$less communication than the existing 2 PC protocol against malicious parties. In fact,VerifyMLeven beats the state-of-the-art semi-honest ML secure inference system! We provide a formal theoretical analysis forVerifyMLsecurity and demonstrate its performance superiority on mainstream ML models including ResNet-18 and LeNet.

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhlet al.with a solution (MUSE, Usenix Security'21), whose performance is then substantially improved by Chandranet al.'s work (SIMC, USENIX Security'22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. Based on this, we propose SIMC 2.0, which complies with the underlying structure of SIMC, but significantly optimizes both the linear and non-linear layers of the model. Specifically, (1) we design a new coding method for parallel homomorphic computation between matrices and vectors. (2) We reduce the size of the garbled circuit (GC) (used to calculate non-linear activation functions,e.g., ReLU) in SIMC by about two thirds. Compared with SIMC, our experiments show that SIMC 2.0 achieves a significant speedup by up to$17.4\times$for linear layer computation, and at least$1.3\times$reduction of both the computation and communication overhead in the implementation of non-linear layers under different data dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by$2.3\sim 4.3\times$over SIMC on different state-of-the-art ML models.

Threshold anonymous credentials enable users to acquire credentials in a decentralized manner while upholding their privacy. However, distributed network environments, such as electronic voting systems and federated identity management systems, have pressing needs for enhancing security, reducing reliance on fixed-group issuers, and achieving scalability. These requirements expose the significant constraints of existing threshold anonymous credential systems, which struggle to support dynamic threshold settings. This struggle leads to the necessity of system rewinding whenever an issuer is included or excluded. Moreover, the communication and computation complexities involved in showing credentials exhibit a linear relationship with the number of credentials possessed by each user. In this paper, we present a novel dynamic threshold anonymous credential system, named DTACB, to tackle the aforementioned challenges. DTACB enables the dynamic adjustment of thresholds, allowing issuer adjustments without rewinding the system. DTACB additionally supports batch-showing of credentials and proof of credential quantity values while preserving the user’s credentials collection remains undisclosed. We conduct rigorous security analysis and validate our efficiency claims via implementing and benchmarking. In particular, DTACB effectively reduces the cost of batch-proof verification to 3.78 ms, independent of the user’s proof size.

Cloud computing is the widespread acceptance of a promising paradigm offering a substantial amount of storage and data services on demand. To preserve data confidentiality, many cryptosystems have been introduced. However, current solutions are incompatible with the resource-constrained end-devices because of a variety of vulnerabilities in terms of practicality and security. In this paper, we propose a practical and secure data-sharing system by introducing a new design of attribute-based encryption with verifiable outsourced decryption (VO-ABE for short). Our system offers: (1) data sharing at a fine-grained level; (2) a scalable key issuing protocol without any secure channel; (3) a verifiable outsourced decryption mechanism for resource-constrained end-devices against the malicious cloud service provider; and (4) adaptive security against the real-world attacks. To formalize our solution with cryptographic analysis, we present the formal definition of VO-ABE and its concrete construction with provable security. In particular, our design leverages the techniques of the traditional ABE, verifiable outsourced decryption, and randomness extractor to support fine-grained access control, cost-effective data sharing, and security assurance with high entropy. Moreover, our design is provably secure in the adaptive model under the standard assumption, which offers a stronger security guarantee since the state-of-the-art solution is selectively secure under the non-standard assumption and suffers from a variety of real-world attacks. The implementation and evaluation demonstrate that our solution enjoys superior functionality and better performance than the relevant solutions. More importantly, our solution is compatible with the resource-constrained end-devices since the decryption mechanism takes around 1.1ms and is 22.7x faster than the state-of-the-art solution.

Blockchain as an open and immutable ledger is being posited as the next frontier in healthcare that will help solve the industry's interoperability challenges. However, immutability in processing personal data is no longer legal since the General Data Protection Regulation (GDPR) requires the “right to be forgotten” as a critical data subject right. To observe such data regulation, it is desirable to build a healthcare blockchain with data redaction in a controlled way. Moreover, electronic health records (EHRs) usually are sensitive and the conventional blockchain lacks systematic and formal security analysis of data confidentiality, especially in the multi-user setting. Furthermore, EHRs are typically helpful in medical research for predicting epidemic diseases and valuable in insurance agencies making business plans. Hence, in healthcare blockchain systems, data confidentiality and flexible key distribution have become the most challenging issues that should be urgently resolved. In this paper, we propose a privacy-preserving and redactable healthcare blockchain system (PRHBS). Our solution offers fine-grained block-level data reduction and secure data sharing with flexible key distribution mechanisms. We give the formal definition and security models of PRHBS, and propose a generic construction based on trapdoor-based chameleon-hash function, attribute-based encryption, and puncturable encryption. We present formal security analysis and give an instantiation based on our proposed generic construction. The comprehensive comparison and experimental simulation demonstrate that our implementation exhibits comparable performance, while surpassing the most relevant solutions in terms of functionality.

In cloud data sharing systems, Searchable Encryption (SE) schemes ensure data confidentiality with retrieving, but it faces several issues in practice. First, most of the previous Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) systems enable users to initiate search requests with a single keyword, which results in many inaccurate results to be returned, thereby wasting computing and bandwidth resources. Second, untrusted cloud servers may return a small portion of incomplete search results to compress communication overhead. Besides, most CP-ABKS schemes only support an unshared multi-owner setting, which incurs a large amount of computational and storage overhead. Furthermore, when the keyword space is a polynomial, most of the previous schemes suffer from offline keyword guessing attacks. To address these issues, we focus on a multi-keyword search scheme which supports the verification of search results without losing efficiency by combining Ciphertext Policy Attribute-Based Encryption (CP-ABE) technology under the shared multi-owner mechanism. We show the security of our scheme, which achieves selective security against offline keyword guessing attacks and guarantees the unforgeability of signatures. The comparison of experimental results illustrates that our scheme is effective and enjoys superior functionalities than the most relevant solutions.

In this paper, we address the problem of privacy-preserving federated neural network training with$N$users. We presentHercules, an efficient and high-precision training framework that can tolerate collusion of up to$N-1$users.Herculesfollows the POSEIDON framework proposed by Sav et al. (NDSS’21), but makes a qualitative leap in performance with the following contributions: (i) we design a novel parallel homomorphic computation method for matrix operations, which enables fast Single Instruction and Multiple Data (SIMD) operations over ciphertexts. For the multiplication of two$h\times h$dimensional matrices, our method reduces the computation complexity from$O(h^{3})$to$O(h)$. This greatly improves the training efficiency of the neural network since the ciphertext computation is dominated by the convolution operations; (ii) we present an efficient approximation on the sign function based on the composite polynomial approximation. It is used to approximate non-polynomial functions (i.e.,ReLUandmax), with the optimal asymptotic complexity. Extensive experiments on various benchmark datasets (BCW, ESR, CREDIT, MNIST, SVHN, CIFAR-10 and CIFAR-100) show that compared with POSEIDON,Herculesobtains up to 4% increase in model accuracy, and up to$60\times$reduction in the computation and communication cost.

To reduce the cost of human and material resources and improve the collaborations among medical systems, research laboratories and insurance companies for healthcare researches and commercial activities, electronic medical records (EMRs) have been proposed to shift from paperwork to friendly shareable electronic records. To take advantage of EMRs efficiently and reduce the cost of local storage, EMRs are usually outsourced to the remote cloud for sharing medical data with authorized users. However, cloud service providers are untrustworthy. In this paper, we propose an efficient, secure, and flexible EMR sharing system by introducing a novel cryptosystem called dual-policy revocable attribute-based encryption and tamper resistance blockchain technology. Our proposed system enables EMRs to be shared at a fine-grained level and allows data users to detect any unauthorized manipulation. Moreover, the key generation center can revoke malicious users without affecting the honest users. We provide the formal security model as well as the concrete scheme with security analysis. The experimental simulation and experimental analysis of our proposed scheme demonstrate that our proposed system has superior performances to the most relevant solutions.

Most blockchains are designed to be immutable such that an object, e.g., a block or a transaction, is persisted once it has been registered. However, blockchain immutability hinders blockchain development due to the increasing abuse of blockchain storage and legal obligations. To break immutability in a controlled way, Derler et al. (NDSS’19) proposed a redactable blockchain with fine-grained controllable rewriting by introducing the notion of policy-based chameleon hash (PCH). Given a PCH-based object associated with an access policy, a trapdoor holder whose rewriting privileges satisfy the access policy can alter the object. Although this work offers an elegant approach to blockchain rewriting, it lacks accountability. In practice, the trapdoor holders may abuse their rewriting privileges, and even use their chameleon trapdoor to build a device in a blackbox manner to gain illegal profits while avoiding being caught. In this paper, we introduce a new design of PCH with blackbox accountability (PCHA). Blackbox accountability offers not only linkability between any modified object and its modifier, but also traceability that enables a central authority to identify responsible trapdoor holders whose secret keys have contributed to the blackbox device. Besides modeling PCHAs, we present a generic construction of PCHAs with rigorous security proofs. We instantiate a concrete construction of PCHA by introducing a practical attribute-based traitor tracing (ABTT) with adaptive security on prime-order pairing groups. The experimental analysis demonstrates that our PCHA and ABTT schemes have modest overheads and superior functionality to the state-of-the-art solutions. In particular, the price of accountability in key generation, hash, and adaption is almost negligible compared to the state-of-the-art solution.

Cloud computing has been widely accepted as a computing paradigm to offer high-quality data services on demand. However, it suffers from various attacks as the cloud service provider and data owners are not in the same trusted domain. To support data confidentiality, existing cloud-based systems apply cryptographic tools to issue the decryption key to data users to share data in a controlled way. However, fine-grained cloud data sharing still faces many challenges, especially when dealing with dynamic user groups. In this paper, we introduce a secure and efficient cloud-based data-sharing system with fine-grained access control and dynamic user groups. Our system enjoys 1) adaptive security in prime-order groups, 2) forward secrecy against revoked user fetches data generated before being revoked, and 3) decryption key exposure resistance against the compromise of the frequently used decryption key, where the previous solutions only concentrate on one or two above-mentioned properties. More specifically, we introduce two timestamp management mechanisms that manage the timestamp in each ciphertext to support dynamic user groups with forward secrecy. By applying the proposed timestamp management mechanisms, we introduce two novel designs of attribute-based encryption schemes with formal definition and security analyses. The proposed schemes are adaptively secure in prime-order groups under a standard assumption and support decryption key exposure resistance. We conduct theoretical analysis and experimental simulation to demonstrate the outperformance of our solutions.

Bilateral access control model, emerging as a novel paradigm in access control, has garnered extensive deployment within the domain of fog computing. This model offers on-demand data services, enabling the efficient identification of sensitive data without resorting to resource-intensive decryption procedures. Nonetheless, prevailing solutions exhibit impracticalities. Specifically, they fall short in supporting adaptive security, while presuming unwavering trustworthiness of the central authority. In this paper, we introduce a pioneering fine-grained and adaptively secure bilateral access control system through enhancements to the matchmaking attribute-based encryption (MABE) framework. We give a formalized definition of MABE, incorporating desirable security features such as blindness and unlinkability, aimed at capturing potential misconduct by the central authority. We propose a generic construction of MABE, drawing upon attribute-based encryption (ABE) and anonymous credential schemes (ACS), with provable security via formal security reduction in the adaptive model. We present an efficient instantiation of the MABE framework by introducing a practical ACS solution, wherein a cryptographic accumulator is employed to enhance performance. Experimental simulations substantiate that our solution not only has superior functionalities but also demonstrates performance on par with state-of-the-art solutions.

With an increasing number of IoT devices being deployed in healthcare, massive amounts of electronic health records (EHRs) are generated and shared in the cloud. To preserve data privacy, one promising data-sharing tool named attribute-based encryption (ABE) has been widely employed. However, it is a challenge to achieve flexible data sharing without loss of confidentiality when authorized users are dynamic. Another challenge is how to guarantee fleet data access time when resource-limited devices are used. In this article, a dynamic access policy ABE (DAP-ABE) system for EHRs in the cloud is proposed. The cloud server can update the access policy without sensitive information, while decryption keys of authorized users do not need to be updated. Authorized users enjoy approximately 0.07 ms data access by outsourcing the majority of the decryption overhead to the cloud server. Furthermore, a verification procedure is embedded in DAP-ABE to check the identities of patients in the data sharing stage, which ensures that no malicious user can upload invalid EHRs. Extensive experiments demonstrate the feasibility and efficiency of the DAP-ABE system.

Healthcare Internet-of-Things (IoT) is an emerging paradigm that enables embedded devices to monitor patients vital signals and allows these data to be aggregated and outsourced to the cloud. The cloud enables authorized users to store and share data to enjoy on-demand services. Nevertheless, it also causes many security concerns because of the untrusted network environment, dishonest cloud service providers and resource-limited devices. To preserve patients’ privacy, existing solutions usually apply cryptographic tools to offer access controls. However, fine-grained access control among authorized users is still a challenge, especially for lightweight and resource-limited end-devices. In this paper, we propose a novel healthcare IoT system fusing advantages of attribute-based encryption, cloud and edge computing, which provides an efficient, flexible, secure fine-grained access control mechanism with data verification in healthcare IoT network without any secure channel and enables data users to enjoy the lightweight decryption. We also define the formal security models and present security proofs for our proposed scheme. The extensive comparison and experimental simulation demonstrate that our scheme has better performance than existing solutions.

Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preservingapproaches have been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (calledirregular users) may be of low quality. Obviously, in a federated training process, data shared by manyirregular usersmay impair the training accuracy, or worse, lead to the uselessness of the final model. In this article, we propose PPFDL, a Privacy-Preserving Federated Deep Learning framework withirregular users. In specific, we design a novel solution to reduce the negative impact ofirregular userson the training accuracy, which guarantees that the training results are mainly calculated from the contribution of high-quality data. Meanwhile, we exploit Yao's garbled circuits and additively homomorphic cryptosystems to ensure the confidentiality of all user-related information. Moreover, PPFDL is also robust to users dropping out during the whole implementation. This means that each user can be offline at any subprocess of training, as long as the remaining online users can still complete the training task. Extensive experiments demonstrate the superior performance of PPFDL in terms of training accuracy, computation, and communication overheads.

Healthcare Internet-of-Things (IoT) enables lightweight devices to observe patients’ vital signals and outsource them to a remote cloud to enjoy flexible data sharing. However, it faces many security threats as the outsourced data is no longer physically controlled by data owners, and the cloud that hosts the outsourced data is not fully trusted. Many privacy protection technologies have been adopted to solve this problem, among which cryptographic mechanisms have become one of the most promising tools. Unfortunately, current cryptographic mechanisms in healthcare IoT mainly suffer from the following challenges: 1) dynamic user groups for managing users’ accessibility; 2) efficient revocation mechanism to mitigate the burden during user revocation; 3) forward and backward secrecy to ensure session independence in the presence of session key leakage; 4) revocable storage to prevent data users from learning any unauthorized data even the data is authorized before; and 5) information manipulation during data transmission. In this article, we introduce a practical and secure system to address the above problems. Our system provides fine-grained access control with dynamic user groups for optimizing scalability and functionality. We prove that our system is secure against numerous real-world threats. Extensive comparison and experimental analysis demonstrate that our system enjoys superior performance than the state-of-the-art solutions.

Cloud-fog computing is a novel paradigm to extend the functionality of cloud computing to provide a variety of on-demand data services via the edge network. Many cryptographic tools have been introduced to preserve data confidentiality against the untrustworthy network and cloud servers. However, how to efficiently identify and retrieve useful data from a large number of ciphertexts without a costly decryption mechanism remains a challenging problem. In this article, we introduce a cloud-fog-device data sharing system (CFDS) with data confidentiality and data source identification simultaneously based on a new cryptographic primitive named matchmaking attribute-based encryption (MABE) by extending matchmaking encryption in CRYPTO’19. Our solution offers a secure fine-grained bilateral access control that includes (1) fine-grained sender access control, (2) fine-grained receiver access control, (3) sender privacy, and (4) performance optimization via outsourcing data source identification to fog nodes. We give the formal definition and security models of MABE, and present a concrete construction with formal security proofs. We also offer a detailed security analysis of our proposed CFDS against real-world security threats. The extensive comparison and experimental simulation demonstrate that, by immigrating heavy workload to fog nodes, our scheme has better functionalities and performances than the most related solutions.

Immutability has been widely accepted as a fundamental property protecting the security of blockchain technology. However, this property impedes the development of blockchain because of the abuse of blockchain storage and legal obligations. To mitigate this issue, a novel construction of blockchain, calledredactable blockchain, was introduced. It enables a central authority to issue the rewriting privilege to a particular party who can rewrite a registered object, e.g., a block or a transaction, in a controlled way. Unfortunately, the central authority must be fully trusted and is an obvious target suffering from various attacks. In this paper, we introduce a redactable blockchain controlled at a fine-grained level in a decentralized setting. In our solution, the rewriting privilege is issued by multiple authorities for reducing the vulnerability of the centralized setting. To formalize our solution, we introduce a novel cryptographic notion, calleddecentralized policy-based chameleon hash(DPCH), with the formal definition and security model. By applying several simple cryptographic tools, such as chameleon hash, digital signature, and multi-authority attribute-based encryption, we present the generic construction of DPCH along with rigorous security proofs. By applying RSA-based chameleon hash and BLS short signature, we give a practical instantiation of DPCH with performance evaluation. The comprehensive evaluation shows that our solution has superior performance than the state-of-the-art solution.

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as well as dynamic policy-updating. In this article, we design a security-enhanced Attribute Cuckoo Filter (se-ACF) to hide the access policy and propose a new CP-ABE system, called Privacy-Preserving Policy Updating ABE (3PU-ABE), which effectively integrates policy hiding and policy updating. We conduct rigorous security analysis and performance evaluation of 3PU-ABE. The results indicate that 3PU-ABE completely hides the access policy without affecting the decryption, and entails better policy-updating efficiency than similar works.

Precision diagnosis and treatment are blending outcomes of machine learning and the Internet of Medical Things (IoMT). In the diabetes treatment, a medical center acts as a medical service provider (MSP) with patients data from IoMT devices. The MSP calculates the accurate dosage by importing the health index data into a corresponding decision-making model. However, the outsourcing unprotected patient data directly to the MSP suffers privacy leakage. In this paper, we propose a privacy-preserving optimal insulin dosing decision in the IoMT system (PIDM) to assist doctors in their decision-making with the patients privacy. To achieve practicality and confidentiality simultaneously, we design a series of secure and efficient interactive protocols depending on additive secret sharing to perform in one stage of DQN, namely, optimal decision making. Contrasted to the most relevant schemes, no additional trusted party is needed in our PIDM, which makes our system more practical and efficient. The security of PIDM is testified, meanwhile, the system effectiveness, and the overall efficiency of PIDM is demonstrated through theoretical analysis and simulation experiments.

As a versatile system architecture, cloud-fog Internet-of-Things~(IoT) enables multiple resource-constrained devices to communicate and collaborate with each other. By outsourcing local data and immigrating expensive workloads to cloud service providers and fog nodes (FNs), resource-constrained devices can enjoy data services with low latency and minimal cost. To protect data security and privacy in the untrusted cloud-fog environment, many cryptographic mechanisms have been invented. Unfortunately, most of them are impractical when directly applied to cloud-fog IoT computing, mainly due to the large number of resource-constrained end-devices (EDs). In this paper, we present a secure cloud-fog IoT data sharing system with bilateral access control based on a new cryptographic tool called lightweight matchmaking encryption. Our system enforces both sender access control and receiver access control simultaneously and adapts to resource-constrained EDs by outsourcing costly workloads to FNs. We conduct extensive experiments to demonstrate the superior performance of our system to the most relevant solutions in the literature.

Notoriously, immutability is one of the most striking properties of blockchains. As the data contained in blockchains may be compelled to redact for personal and legal reasons, immutability needs to be skillfully broken. In most existing redactable blockchains, fine-grained redaction and effective deletion of harmful data are mutually exclusive. To close the gap, we propose a fine-grained and controllably redactable blockchain with harmful data forced removal. In the scheme, the originator of the transaction has fine-grained control over who can perform the redaction and which portions of the transaction can be redacted. The redaction transaction is performed after collecting enough votes from miners. All users can provide the index of the block containing the harmful data to receive rewards, which are borne by the malicious user who initially posted the data. Miners can forcibly remove the harmful data based on the index. The malicious user will be blacklisted if the reward is not paid within a period of time, and any transaction about such user will not be performed later. In addition, the scheme supports the redaction of additional data and unexpended transaction output (UTXO) simultaneously. We demonstrate that the scheme is secure and feasible via formal security analysis and proof-of-concept implementation.

As a versatile technique, cloud-fog computing extends the traditional cloud server to offer various on-demand data services. Maintaining data confidentiality is one of the most crucial requirements for data services, many cryptosystems have been proposed to reserve information privacy against such an untrusted environment. However, in cloud-fog computing, how to confidentially and efficiently share data and fetch desirable data without expensive data decryption for resource-constrained end-devices is challenging. In this paper, we propose a cloud-fog system for the Internet-of-Things (IoT) ecosystem by introducing a cryptographic primitive called server-aided revocable bilateral attribute-based encryption (SRB-ABE). Our solution is a secure and lightweight bilateral access control system with dynamic user groups, including (1) fine-grained data user and data owner access control simultaneously; (2) outsourced data source identification; (3) server-aided user revocation with publicly updatable ciphertexts; and (4) lightweight data decryption mechanism with one exponentiation computation. We present the formal definition and concrete construction of SRB-ABE with security proofs to build cloud-fog systems. The extensive comparison and experimental analysis demonstrate that our construction has superior functionality and comparable performance than the most relevant solutions.

As an immutable append-only distributed ledger, blockchain allows a group of participants to reach a consensus in an untrustworthy ecosystem. Immutability is a blockchain feature that persists data forever, but it is no longer legal in reality. Blockchain has unchangeable improper contents that violate laws. Moreover, data regulation toward “the right to be forgotten” requires blockchain must be modifiable. To address this problem, redactable blockchain has been introduced to relax immutability in a controlled way. However, once a participant is authorized, she/he can rewrite any content and no penalty for the malicious behavior that hinders the wide deployment of redactable blockchain in practice. In this paper, we introduce a new notion, dubbed k-time modifiable and epoch-based redactable blockchain (KERB) with a monetary penalty to control rewriting privileges and penalize malicious behaviors. Our solution is built up from simple building blocks: digital signatures and chameleon hashes. We give a formal definition and security models of KERB, and present a generic construction along with formal proofs. The extensive comparison and experimental analysis illustrate that our solution enjoys superior functionalities and performances than the state-of-the-art solutions.

Outsourced inference service has enormously promoted the popularity of deep learning, and helped users to customize a range of personalized applications. However, it also entails a variety of security and privacy issues brought by untrusted service providers. Particularly, a malicious adversary may violate user privacy during the inference process, or worse, return incorrect results to the client through compromising the integrity of the outsourced model. To address these problems, we propose SecureDL to protect the model’s integrity and user’s privacy in Deep Neural Networks (DNNs) inference process. In SecureDL, we first transform complicated non-linear activation functions of DNNs to low-degree polynomials. Then, we give a novel method to generate sensitive-samples, which can verify the integrity of a model’s parameters outsourced to the server with high accuracy. Finally, We exploit Leveled Homomorphic Encryption (LHE) to achieve the privacy-preserving inference. We shown that our sensitive-samples are indeed very sensitive to model changes, such that even a small change in parameters can be reflected in the model outputs. Based on the experiments conducted on real data and different types of attacks, we demonstrate the superior performance of SecureDL in terms of detection accuracy, inference accuracy, computation, and communication overheads.

Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy" cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads.

A new trend of using deep reinforcement learning for traffic light control has become a spotlight in the Intelligent Transportation System (ITS). Meanwhile, some promising accomplishments have also been made. However, the traditional intelligent traffic signal control system always collects and transmits vehicle information (e.g., vehicle location, speed, etc.) in the form of plaintext, which would result in the leakage of commuters' privacy. Privacy leaks cause unnecessary trouble for users. For example, the personal interests and health information of the commuter can be inferred based on the driving route of the vehicle, or receive harassment advertisements for surrounding services, etc. In this paper, we propose a privacy-preserving intelligent traffic light control (PTLC) system with Deep Q-network. To achieve practicality and confidentiality simultaneously, a series of secure and efficient interactive protocols is designed depending on additive secret sharing to perform the DQN. Moreover, the security of PTLC is testified, meanwhile, the system effectiveness, and the overall efficiency of PTLC is demonstrated through theoretical analysis and simulation experiments.

E-healthcare cloud remarkably facilitates patients to outsource their electronic health record (EHR) to achieve large-scale information sharing in real-time for improving the efficiency of diagnosis and treatment. Unfortunately, compared with other outsourced data, EHR contains more personal privacy, which makes it more preferred by the adversaries. Besides, in the e-healthcare cloud, there may exist malicious users who deliberately disclose their access privileges to obtain financial benefits. In this paper, we propose a fine-grained EHR sharing scheme in e-healthcare cloud with hidden policy and traceability. Specifically, we present a new black-box traitor tracing scheme based on partial policy hiding attribute-based encryption and it performs fine-grained access control on the encrypted EHR, which not only prevents privacy leakage from access policy but also effectively track malicious users who leak decryption privileges to construct a black-box. Our scheme is proved to be fully secure under the standard model. Performance analysis shows that the scheme can achieve the design goals in terms of storage and computation overhead. In the meantime, it is more efficient than the existing schemes under the composite order group.

Personal Health Record (PHR) plays an important role in realizing the exchange and interaction of medical information as well as in managing chronic diseases. In the recent decade, plenty of researches have been carried out to resolve the secure sharing issue of PHR in the cloud via access control schemes. Attribute-based Encryption (ABE) is a promising technique, which implements access control by introducing the access policy. When ABE is used to construct access control scheme in the cloud, hiding and updating access policy have always been two research hotspots. However, all of the existing solutions only consider one of these two issues separately. How to combine policy hiding and policy updating to achieve secure and efficient access control has always been avoided. Furthermore, the practical deployments of traditional ABE in the medical environment are limited by its small attribute universe. In this paper, we propose a policy hiding and updating ABE (PHU-ABE) based on the Fast Attribute-based Encryption(FAME) to tackle these challenges. We argue the security of PHU- ABE. The experimental results demonstrate that our scheme not only protects users' privacy but also realizes efficient policy updating.

Cloud computing is an emerging computing paradigm that enables users to store their data in a cloud server to enjoy scalable and on-demand services. Nevertheless, it also brings many security issues, since cloud service providers (CSPs) are not in the same trusted domain as users. To protect data privacy against untrusted CSPs, existing solutions apply cryptographic methods (e.g., encryption mechanisms) and provide decryption keys only to authorized users. However, sharing cloud data among authorized users at a fine-grained level is still a challenging issue, especially when dealing with dynamic user groups. In this paper, we propose a secure and efficient fine-grained access control and data sharing scheme for dynamic user groups by: 1) defining and enforcing access policies based on the attributes of the data; 2) permitting the key generation center to efficiently update user credentials for dynamic user groups; and 3) allowing some expensive computation tasks to be performed by untrusted CSPs without requiring any delegation key. Specifically, we first design an efficient revocable attribute-based encryption (ABE) scheme with the property of ciphertext delegation by exploiting and uniquely combining techniques of identity-based encryption, ABE, subset-cover framework, and ciphertext encoding mechanism. We then present a fine-grained access control and data sharing system for on-demand services with dynamic user groups in the cloud. The experimental data show that our proposed scheme is more efficient and scalable than the state-of-the-art solution.