Certificate management is needed for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing certificate management mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of certificate revocation checking (CRC) cannot be guaranteed in the presence of active adversaries; The certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is under constrained networks (e.g., it enters dead zones where direct communication with base stations fails).Worse still, compromising the secret key of a single certificate authority (CA) leads to certificate forgery. In this paper, we propose a forward-secure and privacy-preserving certificate management scheme, dubbed SNCM, for satellite networks, where a forward-secure signature algorithm is used to issue certificates. We utilize a neighboring-assisted forwarding paradigm in SNCM to support CRC in constrained networks. SNCM is secure against adversaries who invalidate CRC results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCM utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the CA and base stations handle CRC tasks from satellites in a cooperative way, which frees the CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCM, implement an SNCM prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Exchange-assisted cloud-based data trading (ECDT) is a promising paradigm in current marketplaces, where an exchange provides underlying trading services while the cloud serves as a fundamental base for data sellers, brokers, and data buyers to enable them to benefit from data trading. However, directly integrating existing commercial cloud services into an exchange system suffers from practicality issues. In existing ECDT systems, the data outsourced to the cloud generally follows an “encrypt-then-outsource” paradigm, and the encrypted database makes it impractical for brokers to generate and deliver on-demand data products to the buyer, thereby hindering subscription-based data trading. In this paper, we propose a secure and efficient data delivery scheme, dubbed ESECDT, for subscription-based ECDT. ESECDT consists of data entrustment and data delivery and supports continuous data entrustment and customized data delivery while freeing the broker from heavy costs in terms of computation and communication. We formally define and prove the security of ESECDT in the random oracle model. We also implement an ESECDT prototype and conduct a comprehensive performance evaluation, which demonstrates the efficiency and practicality of ESECDT.

Pre-trained language models (PTLMs) have become integral to modern natural language processing (NLP), yet their reuse exposes them to supply chain risks such as backdoor attacks. Existing studies assume that attackers target specific downstream tasks, overlooking how a backdoored PTLM behaves when fine-tuned for unrelated applications. In practice, such unintended adaptation can trigger anomalous and inconsistent predictions, revealing the backdoor and compromising its stealthiness. We define this phenomenon asbackdoor complications, i.e., unintended behavioral side effects emerging on non-target tasks. This work presents the first systematic quantification and mitigation of backdoor complications. Through extensive experiments on 3 widely used PTLMs and 15 benchmark datasets, we show that complications are pervasive across both single- and multi-task attack settings, causing triggered outputs to collapse into arbitrary classes. To address this issue, we propose theComplication-Suppressed Backdoor Attack(CSBA), a task-agnostic, multi-objective framework that leverages auxiliary non-target datasets to suppress backdoor complications. CSBA effectively suppresses complications on unseen downstream tasks while maintaining near-perfect attack success rates. Our work reveals a critical side effect in backdoored PTLMs and provides a new perspective on the stealthiness and robustness of model supply chain security.

Single Sign-On (SSO) allows users to sign on to multiple relying providers (RPs) with a single authentication token issued by an identity provider (IdP), which provides users a convenient and efficient way to access multiple services from different RPs. As the security of SSO relies on the reliability of IdP (which needs to well maintain a secret used to issue tokens), it suffers from the single-point-of-failure problem. Existing schemes address the problem by utilizing multiple IdPs to issue tokens in a threshold way, so as to make the task of compromising the secret for adversaries as difficult as possible. However, no security guarantee is considered once the secret is compromised by adversaries. In this paper, we propose a distributed forward-secure SSO scheme, dubbed DFSSO, to achieve security in the “post-compromising case” with minimized costs: after the secret is compromised, only a small portion of users need to re-authenticate themselves with IdPs. The key technique behind DFSSO is a new cryptographic primitive, i.e., threshold forward-secure signature, which is interesting in its own right. We integrate DFSSO into OpenID Connect (i.e., OIDC, a popular SSO standard), implement a prototype, and conduct a comprehensive performance evaluation, which demonstrates that DFSSO is efficient and practical.

Certificate revocation checking (CRC) is a fundamental component for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing CRC mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of checking results cannot be guaranteed in the presence of active adversaries; the certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is being under constrained networks (e.g., it enters dead zones where direct communication with base stations fails). In this paper, we propose a privacy-preserving and lightweight CRC scheme, dubbed SNCRC, for satellite networks, where a neighboring-assisted forwarding paradigm is utilized to support CRC in constrained networks. SNCRC is secure against adversaries who invalidate checking results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCRC utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the certificate authority (CA) and base stations handle CRC tasks from satellites in a cooperative way, which frees CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCRC, implement an SNCRC prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Electronic voting (e-voting) plays a key role in modern democratic processes, especially in scenarios where consensus or collective decisions need to be reached. Existing e-voting schemes share the same paradigm of "one-person-one-vote" and treat all voters equally, which is unsatisfactory for weighted settings where every voter is associated with a weight. Furthermore, existing schemes suffer from critical threats towards voters’ privacy and voting results, which becomes a major hindrance towards the broad adoption of e-voting schemes in reality. In this paper, we propose a verifiable e-voting scheme, dubbed WEAPT, to support weighted e-voting with a strong security guarantee. The key technique behind WEAPT is a threshold weighted matrix aggregation mechanism with public verifiability and privacy preservation, where the Shamir secret sharing scheme, Pedersen vector commitment scheme, and zeroknowledge proofs are deployed. We provide security analyses to show that WEAPT is secure against internal and external adversaries. We implement a WEAPT prototype and conduct a comprehensive performance evaluation, which demonstrates its practical efficiency.

Recent research highlights concerns about the trustworthiness of third-party Pre-Trained Language Models (PTLMs) due to potential backdoor attacks. These backdoored PTLMs, however, are effective only for specific pre-defined downstream tasks. In reality, these PTLMs can be adapted to many other unrelated downstream tasks. Such adaptation may lead to unforeseen consequences in downstream model outputs, consequently raising user suspicion and compromising attack stealthiness. We refer to this phenomenon as backdoor complications. In this paper, we undertake the first comprehensive quantification of backdoor complications. Through extensive experiments using 4 prominent PTLMs and 16 text classification benchmark datasets, we demonstrate the widespread presence of backdoor complications in downstream models fine-tuned from backdoored PTLMs. The output distribution of triggered samples significantly deviates from that of clean samples. Consequently, we propose a backdoor complication reduction method leveraging multi-task learning to mitigate complications without prior knowledge of downstream tasks. The experimental results demonstrate that our proposed method can effectively reduce complications while maintaining the efficacy and consistency of backdoor attacks.

Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In addition, as a large number of duplicate data files exist in cloud storage, data deduplication is significant to improve storage efficiency. In this article, we propose a blockchain-assisted fine-grained deduplication and integrity auditing scheme for outsourced large-scale data in cloud storage, achieving internal deduplication and cross-user external deduplication for ciphertexts and authentication tags. By constructing sparse summation ciphertext tree, the scheme implements Proofs of Ownership protocol through vector commitment, and guarantees the retrieval of distributed deduplication data blocks by designing the reconstruction matrix and bloom filter. The scheme exploits blockchain and smart contracts to ensure transparent integrity auditing without a third-party auditor (TPA), thereby avoiding malicious auditing biases. Security analysis and performance evaluation demonstrate the feasibility of the scheme for deploying in cloud storage systems.

Syndrome-based early epidemic warning plays a vital role in preventing and controlling unknown epidemic outbreaks. It monitors the frequency of each syndrome, issues a warning if some frequency is aberrant, identifies potential epidemic outbreaks, and alerts governments as early as possible. Existing systems adopt a cloud-assisted paradigm to achieve cross-facility statistics on the syndrome frequencies. However, in these systems, all symptom data would be directly leaked to the cloud, which causes critical security and privacy issues. In this paper, we first analyze syndrome-based early epidemic warning systems and formalize two security notions, i.e., symptom confidentiality and frequency confidentiality, according to the inherent security requirements. We propose extsf{EpiOracle}, a cross-facility early warning scheme for unknown epidemics. EpiOracle ensures that the contents and frequencies of syndromes will not be leaked to any unrelated parties; moreover, our construction uses only a symmetric-key encryption algorithm and cryptographic hash functions (e.g., [CBC]AES and SHA-3), making it highly efficient. We formally prove the security of EpiOracle in the random oracle model. We also implement an EpiOracle prototype and evaluate its performance using a set of real-world symptom lists. The evaluation results demonstrate its practical efficiency.

Exchange-assisted data trading (EADT) has become an essential paradigm in current data marketplaces. With data exchanges, sellers and buyers can trade data in an efficient and convenient way. However, existing EADT systems are vulnerable to privacy violations. Sensitive information about the data owned by sellers (manifested as attributes of the data) and the purchasing requirements of buyers (manifested as interests) are highly susceptible to leakage. On the one hand, buyers and sellers have direct access to the type of data supplied or desired before the data transaction is established. On the other hand, the information about transactions between the seller and buyer is transparent to the exchange, including the content of the transaction contract. In addition, the participants are likely to repudiate the content of previously accepted contracts or trigger a bidding war by contract first authorized by others, which raises threats towards authenticity and fairness. In this paper, we investigate the contract agreement in actual EADT systems, enumerate the inherent requirements of secrecy and fairness, and formally define them. Then we propose a privacy-preserving and fair contract agreement framework, dubbed PFCA, which consists of order-matching, negotiation, and authorization. We further propose a practical instantiation of PFCA, dubbed BestPFCA, utilizing efficient private set intersection (PSI), secure messaging (SM), and three-party signature (TPS). In addition, we also implement a BestPFCA prototype and conduct a comprehensive performance evaluation, which demonstrates the efficiency and practicality of BestPFCA.

Data auditing contributes to checking the integrity of outsourced data, promoting the vigorous development of cloud storage services. In actual scenarios, such as migration of electronic medical records or data transfer of enterprise mergers and acquisitions, it always require data auditing to help clients with dynamic data migration and integrity checking. In this paper, we present an efficient dynamic certificateless outsourced data auditing mechanism supporting multi-ownership transfer (CDA-MOT), addressing the issue of key escrow and without needing complex certificate management. By integrating a certificateless multi-signature on the same data file into the construction of a homomorphic authenticator based on the Lagrange inverse Multinomial theorem, CDA-MOT not only achieves integrity verification but also enables clients to transfer ownership rights and responsibilities for multi-ownership data in collaboration with cloud servers. Utilizing blockchain systems to store necessary data conversion and update records, as well as smart contracts to fulfill auditing tasks, CDA-MOT owns the characteristics of openness, transparency, accountability, and decentralized public auditing. Besides, CDA-MOT could be further applied in the extension of dynamic update operations, even if outsourced data have been transferred. The security analysis and performance evaluation have demonstrated the feasibility of CDA-MOT in the secure deployment of cloud storage.

Recently, several technology companies have released online inference services for clients based on Transformer-based large language models, which show excellent performance in various tasks. However, in these services, the inputs usually involve clients’ sensitive information. To address this problem, many works have proposed secure inference on language models such as GPT. For language models, complex mathematical functions like Gaussian Error Linear Unit (GELU) are used extensively and dominate the main cost of secure inference. In this work, we systematically study the existing secure GELU protocols and classify previous methods into two categories: polynomial-based protocols and lookup table (LUT)-based protocols. We point out several important characteristics and tradeoffs for these two classes of secure GELU protocols. Based on these observations and analysis, we propose a new secure GELU protocol, called Simple. The main technique that Simple uses involves a LUT of small size to retrieve approximate polynomials for fitting residual error functions caused by a crude approximation for GELU, which achieves state-of-the-art (SOTA) overhead and accuracy performance. We conduct extensive experiments and benchmark the previous 6 secure GELU protocols. The experimental comparison shows that our Simple protocol achieves 1.1 ∼ 8784.3× computation and 1.4 ∼ 188.8× communication improvements while reducing 1.2∼80.2× errors.

Cloud-based exchange-assisted data trading (EADT) has become the most important paradigm to trade data, where the exchange builds a bridge between data owners, brokers, and buyers to enable them to gain benefits from data, and cloud storage services serve as a key component to deliver data. With cloud-based EADT, the data can be traded in a customized way and the value of data can be unleashed as much as possible. Despite the great advantages of such a paradigm, critical issues also arise. The data content is confronted with leakage, leading to privacy violation. Conventional encryption can be utilized to resolve this tension, but it makes customized data trading inefficient and even impossible. In this paper, we propose a secure data delivery scheme, dubbed STAGE, for cloud-based EADT. STAGE supports customized data trading while freeing the broker from heavy costs in terms of computation and communication. We formally define the security notions of STAGE and prove that STAGE is secure against various attacks. We also implement a STAGE prototype and conduct a comprehensive performance evaluation to demonstrate its efficiency and practicality.

Image scaling is one of the most common operations in image processing. For instance, it is often conducted before image transferring to preserve resources, image classifiers also require images to be input at a specified size. However, potential threats may come out with the image scaling operation. A recent work called image-scaling attack can change the semantic information of the input image when it is scaled to a specific size. For example, a manipulated image of a sheep may become an image of a wolf when it scales to a specific size. Many works have already demonstrated the effectiveness of this attack and the security risks it poses. However, existing image-scaling attacks only focus on single target with single specific size, and are not applicable to multi-target image-scaling attack. In this paper, we present a multi-target image-scaling attack (MTISA). MTISA can be trained with a single image performs diverse and semantically distinct outputs to fool both human vision and image classifiers. Specifically, to fool human vision, we employ SinGAN to generate semantically different but background-similar samples to serve as the attack target samples. To mislead image classifiers, we employ adversarial attacks to construct adversarial examples to serve as the attack target samples. Finally, we evaluate MTISA on chest X-rays dataset and ImageNet dataset, respectively. The experimental results demonstrate that MTISA achieves high attack success rate against both human vision and image classifiers.

Three-layer data trading, where a data broker collects “data materials” from multiple data owners, and then provides customized data products to buyers, remains the most prevalent paradigm in current data marketplaces. However, a profit-driven broker may generate “low-quality” data products based on scratched data but sell them at a high price. Worse still, a malicious broker would pirate others' data products to disrupt data marketplaces. In this paper, we propose a practical proof of dealership scheme, dubbed PPoD, to resist malicious brokers. The key technique behind PPoD is a redactable certification generation mechanism, which enables a broker to prove its dealership of a customized data product in an efficient way. We provide a formal security proof of PPoD, which demonstrates that various attacks, e.g., piracy and deception, launched by a malicious broker can be thwarted. We also implement a PPoD prototype and conduct a comprehensive performance evaluation to show its efficiency and practicality.

With the increasing prevalence of network cloud storage, an escalating number of users are choosing to entrust their data to the cloud. To guarantee remote data integrity and mitigate irreversible loss in case of a single point of failure, numerous multi-cloud public auditing schemes have been proposed. However, most existing studies primarily focus on storage architectures with multiple copies. In practice, users are required to distribute identical data replicas individually across multiple cloud servers (CSs), resulting in significant communication overhead and substantial consumption of storage resources on these servers. Moreover, there is a lack of secure public auditing schemes that effectively address both fault localization and data recovery challenges. To address these issues and enhance storage data reliability, this paper proposes an identity-based integrity auditing and data recovery scheme with fault localization for multi-cloud storage (hereafter referred to as IB-IADR). Specifically, we design a novel identity-based homomorphic signature to facilitate a lightweight auditing challenge-verification process. Our scheme ensures the uniform distribution of encoded data while minimizing data redundancy across multiple CSs. Additionally, IB-IADR provides robust data recovery capabilities and supports fast and accurate fault localization features, including entity position, file position and data block position. We demonstrate that our scheme is provably secure against forgery attacks on response auditing proofs, based on the hardness assumption of the standard CDH problem and DDH problem. We evaluate the proposed scheme’s performance to demonstrate its utility in multi-cloud storage environments.

In mobile edge computing (MEC) systems, data is frequently transmitted between MEC servers and users holding mobile devices for supporting related services. However, critical threats towards data confidentiality and authenticity are raised: adversaries always attempt to extract data content from the transmission and impersonate others to spread malicious data for profits. Furthermore, users have to store the (secret and public) keys used for data transmission locally. Consequently, only devices maintaining the keys can be utilized to access the services provided by MEC servers, and “portability” cannot be achieved. In this paper, we propose a portable authenticated data transmission scheme (dubbed Biplane) via blockchain for MEC systems. Biplane is based on two techniques. One is a blockchain-based authenticated hybrid encryption mechanism, which guarantees data authenticity and confidentiality without requiring a third party (e.g., a Certificate Authority) to assist the MEC servers in certifying users’ public keys. The other one is a blockchain-based portable key management mechanism, which enables the user to transmit data without maintaining any parameter in her/his local devices. We formally prove that Biplane achieves confidential and authenticated data transmission in the universally composable (UC) framework. We also conduct a comprehensive evaluation to demonstrate that Biplane is efficient.

Single-sign-on (SSO) authentication employs an identity provider (IdP) to provide users with an efficient way to authenticate themselves with different service providers and has been widely applied in digital systems. However, existing SSO authentication schemes suffer from critical issues in terms of security and privacy. Regarding security, most SSO authentication schemes achieve a high convenience at the expense of security and are thereby susceptible to various attacks. Regarding privacy, most existing schemes fail to protect users’ subscription pattern and access pattern against adversaries who can easily extract users’ sensitive information from their authentications and launch subsequent attacks for profits. In this paper, we develop a practical SSO authentication system, dubbed PrivSSO, with the protection of users’ subscription pattern and access pattern. To balance the trade-off between security and convenience, the key technique is a secure “hybrid” key-based authentication mechanism: a long-term key stored in a well-guarded hardware token serves as the “primary” authentication factor (AF) to guarantee strong security; an ephemeral key bound with portable device(s) serves as the “daily-used” AF to achieve high convenience. To protect the subscription pattern and access pattern from leakage, we propose a redactable token generation mechanism, where the users themselves specify what IdP and the service providers can learn from their authentications. We formally define and prove the security of PrivSSO. We also implement a PrivSSO prototype and conduct a comprehensive performance evaluation to demonstrate its practicality.

Timed data delivery is a critical service for time-sensitive applications that allows a sender to deliver data to a recipient, but only be accessible at a specific future time. This service is typically accomplished by employing a set of mailmen to complete the delivery mission. While this approach is commonly used, it is vulnerable to attacks from realistic adversaries, such as a greedy sender (who accesses the delivery service without paying the service charge) and malicious mailmen (who release the data prematurely without being detected). Although some research works have been done to address these adversaries, most of them fail to achieve fairness. In this paper, we formally define the fairness requirement for mailmen-assisted timed data delivery and propose a practical scheme, dubbed DataUber, to achieve fairness. DataUber ensures that honest mailmen receive the service charge, lazy mailmen do not receive the service charge, and malicious mailmen are punished. Specifically, DataUber consists of two key techniques: 1) a new cryptographic primitive, i.e., Oblivious and Verifiable Threshold Secret Sharing (OVTSS), enabling a dealer to distribute a secret among multiple participants in a threshold and verifiable way without knowing any one of the shares; and 2) a smart-contract-based complaint mechanism, allowing anyone to become a reporter to complain about a mailman’s misbehavior to a smart contract and receive a reward. Furthermore, we formally prove the security of DataUber and demonstrate its practicality through a prototype implementation.

We propose a protection mechanism for password-based credential databases maintained by service providers against leakage, dubbed PCDL. In PCDL, each authentication credential is derived from a user’s password and a salt, where a service provider employs a set of key servers to share the salt in a threshold way. With PCDL, an external adversary cannot derive any information about the underlying passwords from a compromised credential database, even if he can compromise some of the key servers. The most prominent manifestation of PCDL is transparency: integrating PCDL with existing password-based authentication schemes does not require users to perform any additional operation (and thereby does not change users’ interaction patterns), yet enhances the security guarantee significantly. PCDL serves as an independent component only deployed on the service provider side to harden the credential database. As such, PCDL is well compatible with existing password-based authentication schemes. We analyze the security of PCDL and conduct a performance evaluation, which shows that PCDL is secure and efficient.

This work investigates fine-grained data distribution in real-world federated learning (FL) applications, wherein training samples are distributed across multiple regions, and different clients within each region possess distinct features of local training samples. Furthermore, the datasets and models in these regions often exhibit heterogeneity, characterized by varying label distributions and model architectures, posing challenges to the model construction process. In this article, we propose a vertical federated learning (VFL) framework, named HeteroVFL, to address the data distribution complexities and overcome the hurdles posed by heterogeneous regions. Besides, we enhance the privacy of HeteroVFL by adopting differential privacy, a privacy-preserving technology by injecting measured noise into data based on a stochastic framework. We compare our HeteroVFL with existing solutions on three real-world datasets in simulations. The results demonstrate that HeteroVFL can achieve over 96% accuracy on MNIST, surpassing the accuracy of 90% in the state-of-the-art VFL benchmarks.

Cloud-assisted intelligent transportation systems depend on cloud computing to provide powerful computing capabilities and big data storage services. As precise intelligent traffic control and dispatch policies are heavily based on real-time traffic information (e.g., unmanned driving test information), any altered data may cause severe consequences. The integrity of outsourced critical traffic control data has been the most concerning security issue. To this end, a lightweight proxy-oriented data integrity checking mechanism has been devised, without incurring substantial certificates management. The mechanism enables a data manager in traffic information control center to delegate the proxy to produce the signatures of encrypted data and outsource them to the cloud server, dramatically alleviating the work intensity of the data manager. By integrating blockchain into the mechanism, it gives assistance to the data manager for validating malicious integrity checking behaviors. The comprehensive security analysis and performance evaluation demonstrate the feasibility of the mechanism in the deployment of cloud-assisted intelligent transportation systems.

In this paper, we investigate actual exchange-assisted data trading systems and point out that the increment of data content in a sensitive dataset always results in the increment of its privacy level, i.e., making the dataset more sensitive than before. As a consequence, data trading always follows an incremental privacy-driven paradigm, where (1) buyers with various requirements would purchase subsets of the data with different privacy levels, and (2) when a buyer purchases a subset of the entire dataset with a higher level of privacy, the subsets with all lower levels of privacy are required (in other words, there is a containment relationship between subsets with different levels of privacy). A notable example is attribute-value type datasets. Based on these observations, we propose a new concept of privacy-driven and fine-grained data trading, which enables sellers and buyers to trade in data in an efficient and flexible way. We propose a concrete instantiation, dubbed PDFG, which enables sellers and buyers to conduct fine-grained data trading with minimal costs in terms of computation and communication. We prove that PDFG is indistinguishable against the chosen plaintext attack (CPA) under the real-or-random (RoR) model. We also conduct a comprehensive performance evaluation to demonstrate the practicality and efficiency of PDFG.

This article analyzes existing smart contract-based public data integrity verification schemes and identifies certain weaknesses. First, the fair arbitration mechanism deployed in these schemes fails to meet the users’ requirements as it may not promptly notify users of data corruption or loss. Second, to ensure outsourced data confidentiality, existing data integrity schemes use a conventional encrypted method, where each user randomly selects a key to encrypt the outsourced data. Such a method results in varying ciphertexts for the same data by different users, leading to additional storage costs for the cloud server. Third, users’ devices, if poorly designed or even intentionally backdoored, can potentially exfiltrate secrets and compromise the security of schemes. To address these issues, we propose the first backdoor-resistant public data integrity verification scheme based on smart contracts (ASSIST). The key idea is to introduce a new entity (a whistleblower) to periodically monitor the state of verification results recorded in the blockchain. This allows for timely notification of data corruption to users. ASSIST requires users to encrypt their data with a cryptographic primitive called message-locked encryption (MLE), which motivates different users to produce the same ciphertext for the same data and reduces storage costs for cloud servers. We also deploy a cryptographic reverse firewall between users’ devices and the external to rerandomize interactive messages, making the exfiltration impossible. We provide rigorous security proofs to demonstrate the security of ASSIST. The performance evaluation shows that ASSIST is efficient regarding computation and communication costs.

Certificate revocation checking (CRC) is a fundamental requirement in certificate-based public-key cryptographic systems. Most existing CRC schemes are not tailored for edge-cloud computing systems, and directly applying these schemes would cause security and efficiency problems. In this article, we first propose a two-layer edge-cloud-assisted CRC framework, dubbed ECA-CRC, where edge nodes utilizing a probabilistic checking algorithm serve as a first layer, and the cloud server utilizing a deterministic checking algorithm serves as a second layer. Both the edge nodes and the cloud server collaboratively provide verifiable CRC services for devices. The most prominent manifestations of ECA-CRC are that: 1) most CRC requests can be processed with the probabilistic checking layer, which reduces the checking delay significantly while providing an accurate CRC service and 2) devices can detect the irresponsible behavior of the service provider, including using an incorrect revoked certificate set (RCS) to compute checking results or procrastinating on updating the RCS, as soon as possible. We then propose an efficient instantiation of ECA-CRC, dubbed eECA-CRC, by utilizing a Merkle hash tree (MHT)-based homomorphic signature, Cuckoo filter, and Othello. We formally prove the security of eECA-CRC against the irresponsible service provider under the random oracle model. We implement an eECA-CRC prototype and conduct a comprehensive performance evaluation based on a public certificate database. Our results show that 95% of CRC requests are completed on the edge nodes, and only 5% of CRC requests need to be handled by the cloud server.

In this paper, we propose a servers-aided password-based subsequent-key-locked encryption mechanism to ensure the confidentiality of outsourced electronic health records (EHRs). The encryption mechanism achieves conditional forward transparency: a doctor can only access a patient's EHRs related to the current diagnosis with the patient's delegation. It also achieves portability: to delegate a doctor for accessing a specific part of EHRs, the patient only needs to send one key (at most 256 bits) in addition to the delegation information to the doctor; the patient does not need to maintain any secret in a local device. Then, we propose a blockchain-based secure EHR provenance mechanism, where a data structure of EHR provenance record is designed to precisely reflect the EHRs’ provenance information; a smart contract on a public blockchain is deployed to secure both EHRs and the corresponding provenance records. Finally, we develop a cloud-based eHealth system, dubbed HealthFort, based on the two mechanisms. Security analysis and comprehensive performance evaluation are conducted to demonstrate that HealthFort is secure and efficient.

In this paper, we introduce a concept of transparent integrity auditing and propose a concrete scheme based on the blockchain, which goes one step beyond existing public auditing schemes, since the auditing does not rely on third-party auditors while freeing users from heavy communication costs on auditing the data integrity. Then we construct a secure transparent deduplication scheme based on the blockchain that supports deduplication over encrypted data and enables users to attest the deduplication pattern on the cloud server. Such a scheme allows users to directly benefit from data deduplication and protects data content against anyone who does not own the data. Finally, we integrate the proposed transparent integrity auditing scheme and transparent deduplication scheme into one system, dubbed BLIND. We evaluate BLIND from security and efficiency, which demonstrates that BLIND achieves a strong security guarantee with high efficiency.

In mobile edge computing (MEC) systems, data is frequently transmitted between MEC servers and mobile devices for supporting related services. However, critical threats towards data confidentiality and authenticity are raised, where adversaries always attempt to extract data content from the transmission and impersonate others to spread malicious data for profits. In this paper, we propose a blockchain-based authenticated data transmission scheme, dubbed Badge, to establish secure channels between MEC servers and mobile devices. Badge is based on a blockchain-based authenticated hybrid encryption mechanism, which frees MEC servers from maintaining devices’ certificates and allows them to encrypt/decrypt a large volume of data in a highly efficient way. We present security analysis to demonstrate that Badge achieves data confidentiality and authenticity. We conduct a comprehensive evaluation to demonstrate that Badge is efficient and practical to deploy.

Privacy-preserving vertical federated learning (VFL) has been widely applied in electronic health (eHealth) systems. However, existing VFL schemes rarely consider the data pre-processing step including feature selection, which will lead to poor convergence rate and even damaging the model utility. In this paper, we propose an efficient and privacy-preserving feature selection scheme for VFL. Specifically, we first propose a general Gini-impurity based feature selection framework, which is compatible with most existing machine learning models in VFL. With the framework, we present two concrete protocols (dubbed πSS−FSand πH−FS, respectively) customized for different eHealth scenarios. πSS−FSexploits a lightweight additive secret sharing technique, such that it can be executed in comparable time as the evaluation of the plaintext scheme. πH−FSis a hybrid feature selection protocol that additionally utilizes a linear homomorphic encryption technique, to reduce the communication overhead at the cost of a moderate runtime. Moreover, extensive evaluations conducted on real-world medical datasets demonstrate that our scheme realizes up to 27% accuracy gains.

In this article, we propose SPADE, an encrypted data deduplication scheme that resists compromised key servers and frees users from the key management problem. Specifically, we propose a proactivization mechanism for the servers-aided message-locked encryption (MLE) to periodically substitute key servers with newly employed ones, which renews the security protection and retains encrypted data deduplication. We present a servers-aided password-hardening protocol to resist dictionary guessing attacks. Based on the protocol, we further propose a password-based layered encryption mechanism and a password-based authentication mechanism and integrate them into SPADE to enable users to access their data only using their passwords. Provable security and high efficiency of SPADE are demonstrated by comprehensive analyses and experimental evaluations.

In this article, we analyze the inherent characteristic of smart grid systems, where we observe that a smart meter always generates different types of electricity consumption data for one user, and a control center (CC) always conducts an in-depth statistic analysis on these data for subsequent services. Among these data, some of them are very sensitive and should be prevented for any leakage and modification. Furthermore, due to the large number of users in a smart grid system, it is advantageous for CC to receive and process the data from different users simultaneously. To this end, we propose a verifiable privacy-preserving multi-type data aggregation scheme (VPMDA) for smart grids. VPMDA enables an aggregator gateway (AG) to aggregate encrypted multi-type data and forward the aggregated data to CC, such that CC checks the integrity of aggregated data and obtains the statistic analysis results (e.g., average, variance) on the aggregated data without learning each individual data content. We further extend VPMDA to improve the performance of verifying data integrity on CC significantly. We formally prove the security of VPMDA against various attacks. We also implement a prototype of VPMDA and conduct a comprehensive performance evaluation to demonstrate its feasibility and efficiency.

We investigate existing “password+hardware token”-based authentication schemes deployed in real-world applications and observe that they are vulnerable to critical threats. Specifically, a compromised manufacturer may issue a backdoored hardware token to a user and later recover the user’s secret, which is well known as backdoor attacks. Additionally, an authentication credential in these schemes consists of two parts: the one is derived from the password, the other one is derived from the hardware token. However, since the two parts are independent of each other, if an adversary can physically access the hardware token of a victim, he is able to break security of these schemes by performing dictionary-guessing attacks (DGA), which is called mislaying-then-DGA. In this paper, we design a non-interactively re-randomizable reverse firewall signature mechanism for securing hardware tokens, such that the user’s secret is well protected even if a backdoor is embedded. We also utilize a servers-aided password-based encryption mechanism to harden hardware tokens, so as to “seamlessly” integrate the two factors into one credential. Based on the above mechanisms, we develop a secure two-factor authentication scheme, dubbed ATTACH. We evaluate ATTACH in terms of security and efficiency to demonstrate it achieves a strong security guarantee with high efficiency.

Public verification enables cloud users to employ a third party auditor (TPA) to check the data integrity. However, recent breakthrough results on quantum computers indicate that applying quantum computers in clouds would be realized. A majority of existing public verification schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with quantum computers in the near future. Moreover, new security issues need to be solved when an original data owner is restricted or cannot access the remote cloud server flexibly. In this paper, we propose an efficient identity-based data outsourcing with public integrity verification scheme (DOPIV) in cloud storage. DOPIV is designed on lattice-based cryptography, which achieves post-quantum security. DOPIV enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server. Any TPA can perform data integrity verification efficiently on behalf of the original data owner, without retrieving the entire data set. Additionally, DOPIV possesses the advantages of being identity-based systems, avoiding complex certificate management procedures. We provide security proofs of DOPIV in the random oracle model, and conduct a comprehensive performance evaluation to show that DOPIV is more practical in post-quantum secure cloud storage systems.

In this paper, we propose an efficient private set intersection protocol, named LL-PSI, to enable two parties (where each party has an individual set) to obtain the intersection of their sets without leaking other information about their sets to each other. Compared with existing protocols, LL-PSI reduces the computational latency of the intersection between two sets significantly at the expense of communication costs between the parties. Based on LL-PSI, we propose a privacy-preserving friend matching scheme for mobile social networks, dubbed PAIRING. PAIRING allows users to match with those who have common interests while preserving users' private information against the semi-honest server and curious users. We analyze the security of PAIRING and conduct a comprehensive performance evaluation, which demonstrates that PAIRING is secure and efficient.

In this paper, we investigate actual cloud-assisted electronic health (eHealth) systems in terms of security, efficiency, and functionality. Specifically, we propose a password-based subsequent-key-locked encryption mechanism to ensure the confidentiality of outsourced electronic health records (EHRs). We also propose a blockchain-based secure EHR provenance mechanism by designing the data structure of the EHR provenance record and deploying a public blockchain and smart contract to secure both EHRs and their provenance records. With the two mechanisms, we develop BESURE (blockchain-based cloud-assisted eHealth system with secure data provenance) to provide a secure EHR storage service with efficient provenance. Security analysis and comprehensive performance evaluation are conducted to demonstrate that BESURE is secure and efficient.

In this article, we analyze the inherent characteristics of smart grids, and point out that some electricity consumption data are very sensitive and should be encrypted. However, once the corresponding private key is compromised, the content of encrypted data would be leaked, thereby violating users' privacy. Additionally, since a control center (CC) is always required to conduct accurate statistic analysis on these data for subsequent services, it is highly demanded for CC to check the integrity of encrypted data. To this end, based on a modified Boneh-Goh-Nissim (BGN) cryptosystem, we propose a key-leakage resilient encrypted data aggregation (KLR-EDA) scheme with lightweight verification in fog-assisted smart grids. KLR-EDA enables each fog node to aggregate first-level verifiable encrypted data from smart meters in the same grid area, and forward them to the cloud server (CS) for long-term storage. Upon receiving flexible challenging list of fog nodes from CC, CS produces second-level verifiable encrypted aggregated data and returns the results to CC. KLR-EDA enables CC to check the integrity of encrypted aggregated data efficiently, and further obtain the statistic analysis results on the aggregated data without learning any information of individual user. In particular, even the private key of CC is exposed or compromised, any adversary cannot break users' privacy. We provide security analysis of KLR-EDA, and conduct performance evaluation to demonstrate its lightweight statistical analysis and verification advantages on the CC side.

The deployment of cloud storage services has significant benefits in managing data for users. However, it also causes many security concerns, and one of them is data integrity. Public verification techniques can enable a user to employ a third-party auditor to verify the data integrity on behalf of her/him, whereas existing public verification schemes are vulnerable toprocrastinating auditorswho may not perform verifications on time. Furthermore, most of public verification schemes are constructed on the public key infrastructure (PKI), and thereby suffer from certificate management problem. In this paper, we propose acertificatelesspublicverification scheme againstprocrastinatingauditors (CPVPA) by usingblockchain technology. The key idea is to require auditors to record each verification result into a transaction on a blockchain. Because transactions on the blockchain are time-sensitive, the verification can be time-stamped after the transaction is recorded into the blockchain, which enables users to check whether auditors perform the verifications at the prescribed time. Moreover, CPVPA is built on certificateless cryptography, and is free from the certificate management problem. We present rigorous security proofs to demonstrate the security of CPVPA, and conduct a comprehensive performance evaluation to show that CPVPA is efficient.

Cloud storage enables users to outsource data to storage servers and retrieve target data efficiently. Some of the outsourced data are very sensitive and should be prevented for any leakage. Generally, if users conventionally encrypt the data, searching is impeded. Public-key encryption with keyword search (PEKS) resolves this tension. Whereas, it is vulnerable to keyword guessing attacks (KGA), since keywords are low-entropy. In this paper, we present a secure PEKS scheme called SEPSE against KGA, where users encrypt keywords with the aid of dedicated key servers via a threshold and oblivious way. SEPSE supports key renewal to periodically replace an existing key with a new one on each key server to thwart the key compromise. Furthermore, SEPSE can efficiently resist online KGA, where each keyword request made by a user is integrated into a transaction on a public blockchain (e.g., Ethereum), which allows key servers to learn the number of keyword requests made by the user without requiring a synchronization between them for per-user rate limiting. Security analysis and performance evaluation demonstrate that SEPSE provides a stronger security guarantee compared with existing schemes, at the expense of acceptable computational costs.

Wireless body area networks (WBANs) rely on powerful cloud storage services to manage massive medical data. As precise medical diagnosis analysis is heavily based on these medical data, any altered medical data may cause severe consequences, the integrity of outsourced medical data has become the most concerning security issue. Up to date, most existing public auditing mechanisms have been proposed to check the data integrity, but they could not achieve conditional identity privacy, any patient would not like others to know his/her real identity corresponding to certain serious disease, and some malicious patients should be revoked timely due to misbehaviors. Additionally, they are vulnerable to malicious auditors, by colluding with the cloud server to cheat patients. In this paper, we propose a conditional identity privacy-preserving public auditing (CIPPPA) mechanism for cloud-based WBANs. CIPPPA is the first public auditing mechanism achieving conditional identity privacy of patients in WBANs, the real identity of a patient is unknown to anyone in cloud-based WBANs other than the private key generator (PKG). We attempt to integrate Ethereum blockchain into CIPPPA, which gives assistance to patients for validating malicious auditing behaviors. Formal security analysis and performance evaluation demonstrate that CIPPPA is practical for cloud-based WBANs.

Cloud-assisted Industrial Internet of Things (IIoT) relies on cloud computing to provide massive data storage services. To ensure the confidentiality, sensitive industrial data need to be encrypted before being outsourced to cloud storage server. Public-key encryption with keyword search (PEKS) enables users to search target encrypted data by keywords. However, most existing PEKS schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with quantum computers in the near future. Moreover, they suffer from key exposure, and thus the security would be broken once the keys are compromised. In this paper, we propose a forward secure PEKS scheme (FS-PEKS) based on lattice assumptions for cloud-assisted IIoT, which is post-quantum secure. We integrate a lattice-based delegation mechanism into FS-PEKS to achieve forward security, such that the security of the system is still guaranteed even the keys are compromised by the adversaries. We define the first formal security model on forward security of PEKS, and prove the security of FS-PEKS under the model. As the keywords of industrial data are with inherently low entropy, we further extend FS-PEKS to resist insider keyword guessing attacks (IKGA). The comprehensive performance evaluation demonstrates that FS-PEKS is practical for cloud-assisted IIoT.

In this letter, we point out that the privacy-preserving adaptive trapdoor hash authentication tree scheme (published in IEEE TIFS, doi: 10.1109/TIFS.2020.2986879) can be invalidated by an adversarial cloud server: if the outsourced data is arbitrarily modified, the cloud server still can pass the third-party auditor's auditing.

Password-based single-sign-on authentication has been widely applied in mobile environments. It enables an identity server to issue authentication tokens to mobile users holding correct passwords. With an authentication token, one can request mobile services from related service providers without multiple registrations. However, if an adversary compromises the identity server, he can retrieve users' passwords by performing dictionary guessing attacks (DGA) and can overissue authentication tokens to break the security. In this paper, we propose a password-based threshold single-sign-on authentication scheme dubbed PROTECT that thwarts adversaries who can compromise identity server(s), where multiple identity servers are introduced to authenticate mobile users and issue authentication tokens in a threshold way. PROTECT supports key renewal that periodically updates the secret on each identity server to resist perpetual leakage of the secret. Furthermore, PROTECT is secure against off-line DGA: a credential used to authenticate a user is computed from the password and a server-side key. PROTECT is also resistant to online DGA and password testing attacks in an efficient way. We conduct a comprehensive performance evaluation of PROTECT, which demonstrates the high efficiency on the user side in terms of computation and communication and proves that it can be easily deployed on mobile devices.

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Watermarking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters of a DL model, extract and verify the fingerprint from a pirated model, and hence trace the rogue customer who illegally distributed his model for profits. We demonstrate that SecureMark_DL is robust against various attacks including fingerprints collusion and network transformation (e.g., model compression and model fine-tuning). Extensive experiments conducted on MNIST and CIFAR10 datasets, as well as various types of deep neural network show the superiority of SecureMark_DL in terms of training accuracy and robustness against various types of attacks.

We propose Chronos+, an accurate blockchain-based time-stamping scheme for outsourced data, where both the storage and time-stamping services are provided by cloud service providers. Specifically, Chronos+integrates a file into a transaction on a blockchain once the file is created, which guarantees the file's latest creation time to be the time when the block containing the transaction is appended to the blockchain. A sufficient number of consecutive blocks that are latest confirmed on the blockchain is embedded into the file at the creation time. These blocks serve as a time-dependent random seed to prove the earliest creation time, due to blockchains' chain quality property. Chronos+makes the file's timestamp corresponding to a time interval formed by the earliest and latest creation times which are derived from the heights of the corresponding blocks. Due to blockchains' chain growth property, such a height-derived timestamp can ensure that the time intervals' range is within a few minutes so as to guarantee the accuracy. We also point out potential threats towards outsourced time-sensitive files and present security analyses to prove that Chronos+is secure against these threats. Comprehensive performance evaluations demonstrate the efficiency and practicality of Chronos+.

Message-locked encryption (MLE) is a special type of symmetric encryption enabling deduplication over ciphertexts. Since an MLE key is extracted from the message itself, it is vulnerable to brute-force attacks. Existing schemes employ an independent key server to help in generating MLE keys, where the MLE key is extracted from the message and a server-side secret to thwart brute-force attacks. Whereas, the security of these schemes depends on the reliability of the key server, which causes the single-point-of- failure problem. In this paper, we propose DECKS, an encrypted data \underline{de}duplication scheme against the \underline{c}ompromised \underline{k}ey \underline{s}erver. DECKS employs multiple key servers to assist users in generating MLE keys using an oblivious and threshold-based protocol, such that compromising any key server would not break the security. To free DECKS from trusting a specific group of key servers during the lifetime of protected data, the key servers are periodically replaced by new ones to renew the security protection. Provable security and high efficiency of DECKS are demonstrated by comprehensive analyses and experimental evaluations.

It is common to certify when a file was created in digital investigations, e.g., determining first inventors for patentable ideas in intellectual property systems to resolve disputes. Secure time-stamping schemes can be derived from blockchain-based storage to protect files from backdating/forward-dating, where a file is integrated into a transaction on a blockchain and the timestamp of the corresponding block reflects the latest time the file was created. Nevertheless, blocks' timestamps in blockchains suffer from time errors, which causes the inaccuracy of files' timestamps. In this paper, we propose an accurate blockchain-based time-stamping scheme called Chronos. In Chronos, when a file is created, the file and a sufficient number of successive blocks that are latest confirmed on blockchain are integrated into a transaction. Due to chain quality, it is computationally infeasible to pre-compute these blocks. The time when the last block was chained to the blockchain serves as the earliest creation time of the file. The time when the block including the transaction was chained indicates the latest creation time of the file. Therefore, Chronos makes the file's creation time corresponding to this time interval. Based on chain growth, Chronos derives the time when these two blocks were chained from their heights on the blockchain, which ensures the accuracy of the file's timestamp. The security and performance of Chronos are demonstrated by a comprehensive evaluation.

In this paper, we analyze the inherent characteristic of electronic medical records (EMRs) from actual electronic health (eHealth) systems, where we found that first, multiple patients would generate large amounts of duplicate EMRs and second, cross-patient duplicate EMRs would be generated numerously only in the case that the patients consult doctors in the same department. We then propose the first efficient and secure encrypted EMRs deduplication scheme for cloud-assisted eHealth systems (HealthDep). With the integration of our analysis results, HealthDep allows the cloud server to efficiently perform the EMRs deduplication, and enables the cloud server to reduce storage costs by more than 65% while ensuring the confidentiality of EMRs. Security analysis shows that HealthDep provides a stronger security guarantee than Marforio et al.'s scheme (NDSS 2014) and Bellare et al.'s scheme (USENIX Security 2013). Algorithm implementation and performance analysis demonstrate the feasibility and high efficiency of HealthDep.

Cloud storage services allow users to outsource their data to cloud servers to save local data storage costs. However, unlike using local storage devices, users do not physically manage the data stored on cloud servers; therefore, the data integrity of the outsourced data has become an issue. Many public verification schemes have been proposed to enable a third-party auditor to verify the data integrity for users. These schemes make an impractical assumption-the auditors have enough computation capability to bear expensive verification costs. In this paper, we propose a novel public verification scheme for the cloud storage using indistinguishability obfuscation, which requires a lightweight computation on the auditor and the delegate most computation to the cloud. We further extend our scheme to support batch verification and data dynamic operations, where multiple verification tasks from different users can be performed efficiently by the auditor and the cloud-stored data can be updated dynamically. Compared with other existing works, our scheme significantly reduces the auditor's computation overhead. Moreover, the batch verification overhead on the auditor side in our scheme is independent of the number of verification tasks. Our scheme could be practical in a scenario, where the data integrity verifications are executed frequently, and the number of verification tasks (i.e., the number of users) is numerous; even if the auditor is equipped with a low-power device, it can verify the data integrity efficiently. We prove the security of our scheme under the strongest security model proposed by Shi et al. (ACM CCS 2013). Finally, we conduct a performance analysis to demonstrate that our scheme is more efficient than other existing works in terms of the auditor's communication and computation efficiency.

Cyber-physical-social system (CPSS) allows individuals to share personal information collected from not only cyberspace but also physical space. This has resulted in generating numerous data at a user's local storage. However, it is very expensive for users to store large data sets, and it also causes problems in data management. Therefore, it is of critical importance to outsource the data to cloud servers, which provides users an easy, cost-effective, and flexible way to manage data, whereas users lose control on their data once outsourcing their data to cloud servers, which poses challenges on integrity of outsourced data. Many schemes have been proposed to allow a third-party auditor to verify data integrity using the public keys of users. Most of these schemes bear a strong assumption: the auditors are honest and reliable, and thereby are vulnerability in the case that auditors are malicious. Moreover, in most of these schemes, an auditor needs to manage users certificates to choose the correct public keys for verification. In this paper, we propose a secure certificateless public integrity verification scheme (SCLPV). The SCLPV is the first work that simultaneously supports certificateless public verification and resistance against malicious auditors to verify the integrity of outsourced data in CPSS. A formal security proof proves the correctness and security of our scheme. In addition, an elaborate performance analysis demonstrates that the SCLPV is efficient and practical. Compared with the only existing certificateless public verification scheme (CLPV), the SCLPV provides stronger security guarantees in terms of remedying the security vulnerability of the CLPV and resistance against malicious auditors. In comparison with the best of integrity verification scheme achieving resistance against malicious auditors, the communication cost between the auditor and the cloud server of the SCLPV is independent of the size of the processed data, meanwhile, the auditor in the SCLPV does not need to manage certificates.