Skyline optimization is a powerful tool for filtering prominent data to support analysis and decision-making. However, traditional centralized skyline predicates are inadequate for contemporary data islands, and shallow data federation poses a threat to privacy with sensitive data. In existing distributed environments, achieving both efficiency and security in skyline computation remains a critical challenge. This paper addresses the challenge of performing secure skyline predicates on encrypted data federation while safeguarding both the dataset and skyline from unauthorized access. We propose a novel asynchronous structured skyline predicate based on vertical dominance and truth-value conversion, taking full advantage of distributed computing. Furthermore, we introduce a secure optimization that balances security and efficiency, thereby facilitating a distributed skyline predicate. We evaluate the efficiency and scalability across various parameters, demonstrating improvements in traversal overhead and expensive ciphertext operations.

With the increasing demand for access to and query of multimedia data, many applications rely on outsourcing data storage and queries to the cloud. Although efficient, this approach poses privacy risks to data owners. Researchers have sought secure methods for processing outsourced data, with the prerequisite of securely retrieving target data from datasets. In this paper, we address the performance limitations of existing three-party oblivious selection algorithms by proposing a novel online-efficient design. Furthermore, we optimize the proposed approach through algorithmic and system-level enhancements, achieving performance improvements across various settings.

Fine-tuning is an essential process to improve the performance of Large Language Models (LLMs) in specific domains, with Parameter-Efficient Fine-Tuning (PEFT) gaining popularity due to its capacity to reduce computational demands through the integration of low-rank adapters. These lightweight adapters, such as LoRA, can be shared and utilized on open-source platforms. However, adversaries could exploit this mechanism to inject backdoors into these adapters, resulting in malicious behaviors like incorrect or harmful outputs, which pose serious security risks to the community. Unfortunately, few current efforts concentrate on analyzing the backdoor patterns or detecting the backdoors in the adapters. To fill this gap, we first construct and release PADBench, a comprehensive benchmark that contains 13, 300 benign and backdoored adapters fine-tuned with various datasets, attack strategies, PEFT methods, and LLMs. Moreover, we propose PEFTGuard, the first backdoor detection framework against PEFT-based adapters. Extensive evaluation upon PADBench shows that PEFTGuard outperforms existing detection methods, achieving nearly perfect detection accuracy (100%) in most cases. Notably, PEFTGuard exhibits zero-shot transferability on three aspects, including different attacks, PEFT methods, and adapter ranks. In addition, we consider various adaptive attacks to demonstrate the high robustness of PEFTGuard. We further explore several possible backdoor mitigation defenses, finding fine-mixing to be the most effective method. We envision that our benchmark and method can shed light on future LLM backdoor detection research.11Our code and dataset are available at: https://github.com/Vincent-HKUSTGZ/PEFTGuard.

TLS 1.3 is widely used to realize secure communication over the Internet. Existing security analyses of TLS 1.3 primarily focus on its handshake protocol which is indeed an authenticated key exchange (AKE) protocol, and implicitly neglect the so-called subversion attacks (e.g., breaking TLS via Dual EC) in the real world. Reverse firewall (RF) is a prevalent approach to defend against subversion attack. To the best of our knowledge, the only two subversion-resilient AKE protocols with RFs are proposed by Dodis et al. (CRYPTO'16) and Bossuat et al. (ESORICS'20). The security of both protocols is proved under game-based model which is insufficient for the concurrent execution of multiple TLS instances in practice. In this paper, we propose$\mathsf {srTLS}$, a variant of the TLS 1.3 full one round-trip time (1-RTT) handshake protocol with RFs under the universally composable (UC) model. In particular, we first present the ideal functionality of unilateral AKE$\mathcal {F}_{\mathsf {uaKE}}$. Then, we use RFs with outer transparency to circumvent the difficulty in sanitizing the messages of handshake protocol, and prove that$\mathsf {srTLS}$UC-realizes$\mathcal {F}_{\mathsf {uaKE}}$in the presence of subversion attacks. Finally, we integrate$\mathsf {srTLS}$and existing subversion-resilient AKE protocols into TLS 1.3. The evaluation result demonstrates that$\mathsf {srTLS}$achieves at least a 44.86% efficiency improvement over other subversion-resilient AKE protocols.

Many network middleboxes have been deployed to performdeep packet inspection(DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted.BlindBox(SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposalPrivDPI(CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposalP2DPI(ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and presentPrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique calleddual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common.

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 ($t = 35$).

Including TOR, most of the anonymous communication systems adopt source routing, that the source has to share the globally consistent view of all relays and maintain the up-to-date information. To increase the scalability of TOR, researchers mainly utilize hop-by-hop routing during circuit extension. However, hop-by-hop routing has not been widely deployed since it suffers from route capture attacks, and most of the countermeasures require the source participate in the route extension indirectly, help verify the selection of next hop by intermediate nodes, thus introduces communication overhead. In this paper, we introduce a novel routing scheme called Jump Routing. In jump routing, the route extension follows the jumping way, that each relay chooses the successor of the next hop rather than the next hop itself. In particular, to the best of our knowledge, we are the first to route in the jumping way. In addition, to defend route capture attacks, enhance data privacy, and defend collusion attacks, we propose multiple schemes including jump verification, jump encryption, and corporative jump verification. Different from previous measures on route capture attacks, jump routingdoes not need the participation of the source, but deals with the attack by intermediate nodes only. We manage to realize the full jump routing prototype, and the evaluation results show that our jump routing is scalable, lightweight, and resilient.

Outsourcing data storage and computations to cloud servers offers a cost-effective solution for remote data management and query processing. However, ensuring the privacy of sensitive information remains a critical concern, and existing secure algorithms rely on data completeness where all attribute values are valid to ignore the dominance issues under intransitivity and cyclicity. This paper addresses the challenge of executing secure skyline predicates on outsourced incomplete data, while keeping the dataset, queries, and results confidential from the cloud servers. We propose a novel secure dominance under incomplete data as a core component of various query types. To balance security and efficiency, we introduce two filtering methods around access patterns. Additionally, we present two secure skyline extensions concerning dimension and skyband to produce meaningful skylines. The proposed solutions are empirically evaluated for efficiency and scalability on diverse datasets, demonstrating the practical viability of our approach.

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the Honest-Majority and Maliciously Skyline Query scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.

To address the confidentiality concerns of malicious adversaries that fully compromise the message broker in pub/sub based IoT systems, several researchers use proxy re-encryption (PRE) to realize end-to-end encrypted message distribution (from publisher to subscriber). However, the all-or-nothing share feature of PRE poses a problem that the share cannot be efficiently revoked. The only way for publishers to revoke the access rights of subscribers is to pick a new public-private key pair and re-generate the re-encryption keys for all the remaining subscribers, which hampers the scalability in practice. To realize efficient user revocation, we present REEDS, an efficient revocable end-to-end encrypted message distribution system for IoT. The core of REEDS is a novel proxy-aided identity-based conditional proxy re-encryption (PIB-CPRE) scheme. Essentially, we use a binary-tree structure to organize re-encryption keys, so that the update of re-encryption keys is reduced from linear to logarithmic in the number of subscribers. We show that REEDS satisfies confidentiality, efficient immediate revocation, decentralized authorization, and maintains low overhead for publishers and subscribers. The prototype system is implemented and its performance is evaluated. The results show that REEDS is not only easy to deploy over existing message brokers but also highly efficient.

Efficiently mining frequent itemsets and association rules on the encrypted outsourced data remains a great challenge for the time-consuming ciphertext computations. Nowadays, it has been not well addressed for privacy-preserving frequent itemsets and association rule mining schemes with mining efficiency, dataset, and query confidentiality simultaneously. In this paper, we investigate the study of privacy issues on frequent itemset mining and association rule mining on outsourced data in a two-cloud model, where the data are encrypted and outsourced by multiple owners holding different public keys. We develop several secure computation protocols based on additively homomorphic cryptosystem and additive secret sharing, which enable the clouds could securely mine the frequent itemsets and association rules. Furthermore, we also design two kinds of frequent itemset and association rule query service models, i.e., service customers query the cloud-mined results, and service customers query with their own decided threshold. The proposed scheme not only supports the mining process on the data encrypted by multiple public keys without compromising the security of the datasets, query data and query results, but also offline users. In addition, the experimental results show that our query scheme is much more efficient than the state-of-the-art work.

Context-aware recommendation systems are of increasing popularity in the digital era to recommend personalized items to users. However, how to ensure user data privacy while remaining high recommendation accuracy is widely considered a challenge. In this work, we propose a privacy-preserving method for the context-aware recommendation system in the two-cloud model. In particular, we first adjust the standard additive secret sharing scheme to support secure negative integers computation, based on which we manage to design secure comparison protocol and division protocols that enjoy desirable security and efficiency. By using these new protocols, we propose a secure and efficient context-aware recommendation system that also supports offline users. Compared with the state-of-the-art, our scheme achieves stronger data privacy preservation by further protecting the intermediate data calculated during the system training. Experimental results on real-world datasets indicate that our scheme is efficient. Notable, our system could achieve more significant performance improvement by running the underlying schemes in parallel.

The Snowden revelations in 2013 showed that user machines running cryptographic protocols might be stealthily corrupted by attackers (e.g., manufacturers and supply-chain intermediaries) who could tamper cryptographic implementations to insert backdoors to undermine cryptographic tools. To formalize such attacks, in CRYPTO 2014, Bellareet al.proposed the notion of Algorithm-Substitution Attack (ASA) which has been extensively studied since then. In this work, we turn to investigate the security of anonymous communication (AC) protocol—a well-known tool to protect user privacy on the Internet—in the case when user machines are corrupted. Specifically, we give a formal treatment of ASAs on the universal mixnet-based AC ($\mathsf{U\text{-}Mix\text{-}AC}$) protocols. We show that ASAs on$\mathsf{U\text{-}Mix\text{-}AC}$protocols could be more dangerous than previously thought by presenting attacks that are extremely powerful. As countermeasure, we adopt cryptographic reverse firewall (CRF), originally proposed by Mironov and Stephens-Davidowitz in EUROCRYPT 2015, to restore the security of$\mathsf{U\text{-}Mix\text{-}AC}$protocols in the presence of ASAs. We also implement proposed AC protocol, ASAs and CRFs for experimental evaluations, and the results show that the execution time of subverted algorithms is almost the same as that of faithful ones and our designed CRFs are effective to guard the security of$\mathsf{U\text{-}Mix\text{-}AC}$protocol.

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting deduplication and enabling users to perform search operations over encrypted data. In addition, it supports both multi-keyword and negative keyword searches. We formulate the security notions of MLSE and prove our scheme satisfies all the security requirements. Moreover, we provide an interesting extension of our construction to support Proof of Storage (PoS). Compared with the existing solutions, MLSE achieves better functionalities and efficiency, and hence enables more versatile and efficient cloud storage service.

Due to the revelations of global-scale pervasive surveillance programs, Internet users have an increasing demand for privacy. However, this is usually undesirable for network service providers because attackers would be able to anonymize themselves and avoid regulation while conducting network attacks. Therefore, network service providers want to hold users accountable and it has been widely considered as a tussle to find a good balance point between the accountability and privacy for the Internet. In this work, we first show that existing representative approaches mainly suffer from narrow-range accountability, low efficiency or risky key management. Motivated by these observations, we propose an efficient network layer protocol called APGS to balance the accountability and privacy. At the core of our APGS is the group signature which, however, is not trivial to apply for the network layer mainly due to the efficiency, revocation, and privacy issues. We manage to overcome these challenges via proposing some novel approaches, including challenge-based cache strategy, scalable verifier-local revocation strategy, and Onion-then-Case strategy. We then evaluate the efficiency of APGS and conclude that in our environment, APGS can generate packets up to 20k pkts/s on a desktop and achieve approximately 80% of IP's goodput at most on a software router.

Anonymous communication networks (ACNs) are intended to protect the metadata during communication. As classic ACNs, onion mix-nets are famous for strong anonymity, in which the source defines a static path and wraps the message multi-times with the public keys of nodes on the path, through which the message is relayed to the destination. However, onion mix-nets lacks in resilience when the static on-path mixes fail. Mix failure easily results in message loss, communication failure, and even specific attacks. Therefore, it is desirable to achieve resilient routing in onion mix-nets, providing persistent routing capability even though node failure. The state-of-theart solutions mainly adopt mix groups and thus need to share secret keys among all the group members which may cause single point of failure. To address this problem, in this work we propose a hybrid routing approach, which embeds the onion mix-net with hop-by-hop routing to increase routing resilience. Furthermore, we propose the threshold hybrid routing to achieve better key management and avoid single point of failure. As for experimental evaluations, we conduct quantitative analysis of the resilience and realize a local T-hybrid routing prototype to test performance. The experimental results show that our proposed routing strategy increases routing resilience effectively, at the expense of acceptable latency.

Abstract Subversion of cryptography has received wide attentions especially after the Snowden Revelations in 2013. Most of the currently proposed subversion attacks essentially rely on the freedom of randomness choosing in the cryptographic protocol to hide backdoors embedded in the cryptosystems. Despite the fact that significant progresses in this line of research have been made, most of them mainly considered the classical setting, while the research gap regarding subversion attacks against post-quantum cryptography remains tremendous. Inspired by this observation, we investigate a subversion attack against existing protocol that is proved post-quantum secure. Particularly, we show an efficient way to undetectably subvert the well-known lattice-based encryption scheme proposed by Regev (STOC 2005). Our subversion enables the subverted algorithm to stealthily leak arbitrary messages to the outsider who knows the backdoor. Through theoretical analysis and experimental observations, we demonstrate that the subversion attack against the LWE encryption scheme is feasible and practical.

Abstract Hierarchical identity-based signature (HIBS) plays a core role in a large community as it significantly reduces the workload of the root private key generator. To make HIBS still available and secure in post-quantum era, constructing lattice-based schemes is a promising option. In this paper, we present an efficient HIBS scheme in polynomial rings. Although there are many lattice-based signatures proposed in recent years, to the best of our knowledge, our HIBS scheme is the first ring-based construction. In the center of our construction are two new algorithms to extend lattice trapdoors to higher dimensions, which are non-trivial and of independent interest. With these techniques, the security of the new scheme can be proved, assuming the hardness of the Ring-SIS problem. Since operations in the ring setting are much faster than those over integers and the new construction is the first ring-base HIBS scheme, our scheme is more efficient and practical in terms of computation and storage cost when comparing to the previous constructions.

In-network middleboxes are vital for the Internet-of-Things (IoT) system security, but the widely adopted transport layer security (TLS) protocol blinds application-level middleboxes due to the encryption of traffic data. To resolve this problem, many solutions have been proposed to date. Among them, SplitTLS is widely adopted in the industry by proxy manufacturers. It requires TLS client to install customized root certificates and incurs additional security flaws, e.g., disabling server authentication and using weak cipher suites. Another approach is to customize the TLS protocol where middleboxes are enabled via either performing handshake directly with TLS endpoints or receiving session key materials in an out-of-band manner. Overall, current solutions would either jeopardize the original TLS handshake procedure or incur additional overheads on the endpoints. In this article, we design a new middlebox-enhanced TLS (ME-TLS), which enables endpoints to introduce authenticated middleboxes into a TLS session while control middleboxes' access permission and processing order of traffic data. Particularly, in our proposed ME-TLS, the handshake structure of TLS keeps unchanged and middleboxes work in a passive manner. That is, middleboxes in the ME-TLS could recover session key materials by monitoring handshake messages passively instead of interacting with endpoints; secondary secure channels for key transmission are also not needed in our ME-TLS. We implement our ME-TLS based on the TLS 1.3 protocol and evaluate its performances. The experimental results demonstrate that our proposal is practical and deployable for real-world IoT scenarios.

Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform for doing machine learning. However, the issue of data privacy is far from being well solved and thus has been a general concern in the cloud-aided machine learning. In this work, we investigate the study of how to efficiently do decision tree training and evaluation in the cloud and meanwhile achieve privacy preservation. Unlike existing cloud server-assisted model training approaches, in our proposed solution, the whole training process is mostly done by the cloud service provider who owns the machine learning model. Since the cloud cannot directly divide the encrypted dataset according to the best attributes selected, we propose a new method for decision tree training without dataset splitting. Precisely, we design three methods for decision tree training with the different tradeoff between privacy and efficiency. In all of these methods, the outsourced data are not revealed to the cloud service provider. We also propose a privacy-preserving decision tree evaluation scheme where the cloud service provider learns nothing about the user's input and the classification result while the trained model is kept secret to the user who could only learn the classification result. Compared with previous decision tree evaluation work, our scheme achieves desirable privacy preservation against both the user and the cloud service provider, and also minimizes the user's computation and communication costs. Moreover, besides protecting the data confidentiality, our proposed scheme also supports off-line users and thus has good scalability. The real-world dataset-based experimental results demonstrate that our system is of desirable utility and efficiency.

Summary Hardware‐based middleboxes are ubiquitous in computer networks, which usually incur high deployment and management expenses. A recently arising trend aims to address those problems by outsourcing the functions of traditional hardware‐based middleboxes to high volume servers in a cloud. This technology is promising but still faces a few challenges from different aspects, including privacy concerns, middlebox functionality, and performance. In this paper, we propose two practical approaches to implementing a cloud‐based DPI middlebox. The outsourced DPI middlebox performs payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Our first approach employs a modified reversible sketch structure, which is used for efficient error‐free membership testing, and our second approach extends the famous AC pattern matching algorithm to the cipher text domain. We utilize unkeyed one‐way hash functions instead of complex cryptographic protocols to achieve the privacy preservation requirements. Our system supports a wide range of real‐world inspection rules. We conduct evaluations on the ClamAV rule set, and the experiment results demonstrate the effectiveness of our proposals.

Nowadays, outsourcing data and machine learning tasks, e.g.,$k$-nearest neighbor (KNN) classification, to clouds has become a scalable and cost-effective way for large scale data storage, management, and processing. However, data security and privacy issue have been a serious concern in outsourcing data to clouds. In this article, we propose a privacy-preserving KNN classification scheme on cloud data in a twin-cloud model based on an additively homomorphic cryptosystem and secret sharing. Compared with existing works, we redesign a set of lightweight building blocks, such as secure square Euclidean distance, secure comparison, secure sorting, secure minimum, and maximum number finding, and secure frequency calculating, which achieve the same security level but with higher efficiency. In our scheme, data owners stay offline, which is different from secure-multiparty computation-based solutions which require data owners’ stay online during computation. In addition, query users do not interact with the cloud except sending query data and receiving the query results. Our security analysis shows that the scheme protects outsourced data security and query privacy, and hides access patterns. The experiments on real-world dataset indicate that our scheme is significantly more efficient than existing schemes.

In vehicular ad hoc networks (VANETs), conditional privacy preserving authentication (CPPA) scheme is widely deployed to solve security and privacy issues. Existing CPPA schemes usually require ideal tamper-proof devices (TPDs) on vehicles which, however, might be infeasible or do not exist in reality due to high security requirements. To address this problem, we propose a practical framework of CPPA scheme that supports more realistic TPDs which are less secure correspondingly. We demonstrate that this framework also manages to achieve nonframeability in addition to other security objectives including nonrepudiation, conditional privacy preserving, and unlinkability. Moreover, performance analysis shows that our framework has better efficiency in authentication. All these features make our framework practical for VANETs.

A revocable ID-based broadcast encryption scheme allows an authorized third party to revoke any receiver (decryptor) from the initial receiver set S of the original broadcast ciphertext without the need of decryption. However, the existing revocable ID-based broadcast encryption schemes in the literature cannot fully preserve the receiver privacy and have a large size of ciphertext when the revoked user sets are large. To solve these problems, in this paper, we propose a novel scheme: fully privacy-preserving ID-based broadcast encryption with authorization. Our scheme allows an authorized party to dynamically handle the decryption rights of receivers via an authorized user set L without knowing the message and the identities of the initial receivers. Only those users who are both in S and L can decrypt the ciphertext successfully. The final ciphertext reveals nothing about the identity information of receivers and the authorized users. Our scheme achieves full collusion resistance and is applicable to anonymous data sharing where the receivers are decided by the authorized third party (or multiple authorized third parties) excluding the data owner. We show that our proposed scheme is provably secure under the defined security models in the random oracle model.

Summary Data search and sharing are two important functionalities in social networks. The social network users can form a peer‐to‐peer group and securely and flexibly search and share cloud data through wireless applications. When the number of users increases, the communication, storage, and computational overheads will be increased, and the quality of services such as searching and data sharing for clients could be affected. In order to solve these problems, we formalize an ID‐based multi‐user searchable encryption (IDB‐MUSE) and formally define its security model, where the security notions accommodate indistinguishability against insider's keyword guessing attack, indistinguishability against chosen keyword attack, and indistinguishability against insider's identity guessing attack. We present an IDB‐MUSE scheme, where the index and search trapdoor are of constant size. We formally prove its security properties. To improve the search efficiency, we divide the computation of the trapdoor into two phases, that is, the offline phase and the online phase. The computation cost for the online phase trapdoor remains constant with respect to the number of users. Based on the IDB‐MUSE scheme, a privacy‐preserving data search and sharing protocol is proposed, where only the authorized user can access the shared group data. It captures the properties of source authenticity, data and search pattern privacy‐preserving, anonymity, and request unlinkability. The experimental results show that the protocol is practical for wireless applications. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we present the notion of recipient-revocable identity-based broadcast encryption scheme. In this notion, a content provider will produce encrypted content and send them to a third party (which is a broadcaster). This third party will be able to revoke some identities from the ciphertext. We present a security model to capture these requirements, as well as a concrete construction. The ciphertext consists of k+3 group elements, assuming that the maximum number of revocation identities is k. That is, the ciphertext size is linear in the maximal size of R, where R is the revocation identity set. However, we say that the additional elements compared to that from an IBBE scheme are only for the revocation but not for decryption. Therefore, the ciphertext sent to the users for decryption will be of constant size (i.e.,3 group elements). Finally, we present the proof of security of our construction.

Abstract Smart grid is an electrical grid that uses digital information and communication technology to gather information. Like other digital systems, security and privacy are crucial for smart grid. However, security and privacy protection will inevitably introduce computational complexity and overhead. As smart grid systems are usually operated in a large scale, computational efficiency is a challenging issue. In this paper, we propose an efficient and secure billing system for smart grid, featuring privacy‐preserving and data aggregation. We show that our system offers better privacy protection and computational efficiency, in comparison with an existing protocol. Our security analysis indicates that our protocol achieves privacy‐preserving on electricity reading aggregation and billing, perfect forward secrecy of system session keys, identity authentication, data integrity, and confidentiality. It also shows that even if we allow the collusion of server and gateways, user privacy can still be achieved. Copyright © 2016 John Wiley & Sons, Ltd.

Public key encryption with keyword search (PEKS) is a well-known cryptographic primitive for secure searchable data encryption in cloud storage. Unfortunately, it is inherently subject to the (inside) offline keyword guessing attack (KGA), which is against the data privacy of users. Existing countermeasures for dealing with this security issue mainly suffer from low efficiency and are impractical for real applications. In this paper, we provide a practical and applicable treatment on this security vulnerability by formalizing a new PEKS system named server-aided public key encryption with keyword search (SA-PEKS). In SA-PEKS, to generate the keyword ciphertext/trapdoor, the user needs to query a semitrusted third-party called keyword server (KS) by running an authentication protocol, and hence, security against the offline KGA can be obtained. We then introduce a universal transformation from any PEKS scheme to a secure SA-PEKS scheme using the deterministic blind signature. To illustrate its feasibility, we present the first instantiation of SA-PEKS scheme by utilizing the Full Domain Hash RSA signature and the PEKS scheme proposed by Boneh et al. in Eurocrypt 2004. Finally, we describe how to securely implement the client-KS protocol with a rate-limiting mechanism against online KGA and evaluate the performance of our solutions in experiments.

Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public key encryption with keyword search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside keyword guessing attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named dual-server PEKS (DS-PEKS). As another main contribution, we define a new variant of the smooth projective hash functions (SPHFs) referred to as linear and homomorphic SPHF (LH-SPHF). We then show a generic construction of secure DS-PEKS from LH-SPHF. To illustrate the feasibility of our new framework, we provide an efficient instantiation of the general framework from a Decision Diffie-Hellman-based LH-SPHF and show that it can achieve the strong security against inside the KGA.

With the widely adopted GPS technology in mobile devices, users enjoy many types of location services. As a recently proposed application, determining the optimal private meeting location with an aid of a location server has been an interesting research topic. The challenge in this paper is due to the requirements of security and privacy, because user locations should not be revealed to the honest-but-curious or semi-trusted location server. Adding the security and privacy protection to a location service will inevitably introduce computational complexity and communication overhead. In order to introduce robust location service and make this location service practical, we propose an efficient optimal private meeting location determination protocol, which needs only one round communication and light computation. Our proposed protocol satisfies the requirement of location privacy against outsiders, the semi-trusted meeting location determination server, and the semi-trusted group users. In order to study the performance of our protocol in a real deployment, we simulate our scheme on smartphones. The simulation results and the performance comparison with another scheme demonstrate its advantages in communication and computation efficiency.

Deduplication is a popular technique widely used to save storage spaces in the cloud. To achieve secure deduplication of encrypted files, Bellare et al. formalized a new cryptographic primitive named message-locked encryption (MLE) in Eurocrypt 2013. Although an MLE scheme can be extended to obtain secure deduplication for large files, it requires a lot of metadata maintained by the end user and the cloud server. In this paper, we propose a new approach to achieve more efficient deduplication for (encrypted) large files. Our approach, named block-level message-locked encryption (BL-MLE), can achieve file-level and block-level deduplication, block key management, and proof of ownership simultaneously using a small set of metadata. We also show that our BL-MLE scheme can be easily extended to support proof of storage, which makes it multi-purpose for secure cloud storage.