Federated Learning (FL) exhibits susceptible to model poisoning attacks, which compromise the availability of the collaboratively trained model by introducing detrimental local updates during the training process. The predominant line of defense against such attacks has been to impose stringent restrictions on clients' model updates. However, this strategy raises new vulnerabilities where the global model can be infiltrated by meticulously crafted malicious perturbations. This vulnerability arises due to the model's inherent sensitivity to perturbations, making it exposed and fragile. In response, this work investigates a novel defensive paradigm centered on model stability-specifically, a model's resilience against perturbations within its parameter space. As a solution, we introduce a new method named Model Stability Defense for Federated Learning (MSDFL), designed to fortify the defense of FL systems against model poisoning attacks. MSDFL utilizes a minmax optimization framework, which is fundamentally linked to empirical risk for exploring the effects of model perturbations. The core aim of our approach is to minimize the norm of the model-output Jacobian matrix without compromising predictive performance, thereby establishing defense through enhanced model stability. Moreover, we propose a refined version of MSDFL, named Holistic Model Stability Defense for Federated Learning (HMSDFL), which considers model stability across all output dimensions of the logits to effectively eradicate the disparity in model convergence speed induced by MSDFL. Extensive experimental results fully demonstrate the fidelity, robustness, compatibility, and self-protection of our methods. The source codes are maintained athttps://github.com/qqoneone/MSDFL.

Dynamic Searchable Symmetric Encryption (DSSE) allows clients to update data and search keywords securely over symmetrically encrypted data on an honest but curious server. Conjunctive DSSE, an attractive type of DSSE with expressive search, enables clients to find data containing multiple keywords simultaneously. However, recently proposed efficient conjunctive DSSE schemes, such as ODXT (in NDSS’21) and SDSSE-CQ (in PETS’25), all rely on cross-tag techniques and suffer from either forward privacy or volume-privacy leakages arising from conjunctive keywords, making them vulnerable to injection or leakage-abuse attacks. In this work, we analyze the aforementioned works in depth and design a new conjunctive DSSE scheme named FDXT. For any search query with multiple keywords, FDXT guarantees the forward privacy of all queried keywords. In contrast, ODXT only maintains the forward privacy of the single and first queried keyword. FDXT also avoids volume leakage compared with SDSSE-CQ. Finally, we compared FDXT with ODXT and SDSSE-CQ in terms of performance on the Crime, Wikipedia, and Enron datasets. The experimental results show that FDXT exhibits good performance, which is comparable to ODXT and significantly better than SDSSE-CQ.

Dynamic Searchable Symmetric Encryption (DSSE) allows secure searches over a dynamic encrypted database but suffers from inherent information leakage. Existing passive attacks against DSSE rely on persistent leakage monitoring to infer leakage patterns, whereas this work targets intermittent observation - a more practical threat model. We propose Peekaboo - a new universal attack framework - and the core design relies on inferring the search pattern and further combining it with auxiliary knowledge and other leakage. We instantiate Peekaboo over the SOTA attacks, Sap (USENIX' 21) and Jigsaw (USENIX' 24), to derive their ''+'' variants (Sap+ and Jigsaw+). Extensive experiments demonstrate that our design achieves >0.9 adjusted rand index for search pattern recovery and ∼90% query accuracy vs. FMA's ∼30% (CCS' 23). Peekaboo's accuracy scales with observation rounds and the number of observed queries but also it resists SOTA countermeasures, with >40% accuracy against file size padding and >80% against obfuscation.

Convolutional Neural Networks (CNNs) that have excelled in diverse computer vision tasks are vulnerable to backdoor attacks, enabling attacker-controlled predictions via specific triggers. Restricted to spatial domains, recent research exploits perceptual traits by embedding triggers in the frequency domain, yielding pixel-level indistinguishable perturbations. In black-box settings, restricted access to model and training process necessitates advanced trigger designs. Current frequency-based attacks manipulate magnitude spectra, introducing discrepancies between clean and poisoned data, though vulnerable to common image processing operations like compression and filtering. In this paper, we propose a robust low-frequency backdoor attack (LFBA) in black-box setup that minimally perturbs spectrum components and maintains the perceptual similarity in spatial space simultaneously. Our methodology capitalizes on the insight that optimal triggers can be located in low-frequency regions to maximize attack effectiveness, robustness against image transformation operations, and stealthiness in dual space. To effectively explore the discrete frequency space, we utilize simulated annealing (SA), a form of evolutionary algorithm, to optimize the properties of trigger including the frequency bands to be manipulated and the perturbation of each band under restricted attack scenario. Extensive experiments on both CNNs and Vision Transformers (ViT) confirm the effectiveness and robustness of LFBA against image processing operations and state-of-the-art backdoor defenses. Furthermore, LFBA exhibits inherent stealthiness in both spatial and frequency spaces, making it resistant to human and frequency inspection.

Gordon et al. systematically studied the Universally Composable (UC) security of Multi-client Verifiable Computation (MVC), in which a set of computationally-weak clients delegate the computation of a general function to an untrusted server based on their private inputs, and proposed a UC-secure scheme ensuring that the protocol remains secure even when arbitrarily composed with other UC-secure instances. However, this scheme imposed a significant computational overhead on clients due to the utilization of fully homomorphic encryption, and the plaintext size scaled linearly with function input size. In this work, we present MVOC, a more efficient UC-secure MVC protocol, that significantly reduces the amortized overhead for clients in both semi-honest and malicious settings, by delegating a larger portion of the computation to the server. We enable clients to verify the garbled circuit before entering the online phase, ensuring security against malicious clients without incurring heavy overhead of compiling a semi-honest protocol into a malicious one. We present the detailed proof and analyze the theoretical complexity of MVOC. Furthermore, we implement our protocol and evaluate the performance, and the results demonstrate that the computation and communication overheads during the input phase can be decreased by at least 95.55% and 87.17%, respectively.

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.

Log anomaly detection aims to discover abnormal events from massive log data to ensure the security and reliability of software systems. However, due to the heterogeneity of log formats and syntaxes across different systems, existing log anomaly detection methods often need to be designed and trained for specific systems, lacking generalization ability. To address this challenge, we propose LogDLR, a novel unsupervised cross-system log anomaly detection method. The core idea of LogDLR is to use universal sentence embeddings and a Transformer-based autoencoder to extract domain-invariant latent representations from log entries, which can effectively adapt to log format changes and capture semantic information and dependencies in log sequences. To obtain domain-invariant latent representations, we adopt a domain-adversarial training strategy, introducing a domain discriminator that competes with the Transformer-based encoder through a gradient reversal layer, forcing the encoder to learn shared knowledge between different system logs. Finally, the Transformer-based decoder detects anomalies based on the domain-invariant representations obtained by the encoder. We evaluate LogDLR in simulated cross-system scenarios using three publicly available log datasets. The experimental results show that LogDLR can handle heterogeneous logs effectively in cross-system scenarios and achieve efficient and accurate anomaly detection on both source and target systems.

Passwords remain one of the most widely used forms of authentication in modern systems. However, their inherent predictability, stemming from common user behaviors in password creation, renders password-based authentication vulnerable to guessing attacks. To balance memorability and security, users often construct isomorphic variants of a base password by altering its structure, such as transforming 123abc into 1a2b3c. These variants pose significant challenges to traditional password guessing models. In particular, mainstream approaches such as Markov model and Probabilistic Context-Free Grammar (PCFG) model struggle to capture the structural relationships among these variants. To address this challenge, we propose PassGIN, a password guessing framework based on Graph Isomorphism Networks (GIN). By modeling a password as a graph, PassGIN captures both local adjacency and character rearrangement patterns, enabling the model to distinguish subtle structural differences between base passwords and their isomorphic variants. To further enhance performance, we introduce PassCluster, a dynamic edge-weighting mechanism that leverages adjacency frequencies observed in large-scale password datasets. This allows GIN to more effectively learn structural variations and generate accurate guesses. Extensive experiments on eight real-world datasets demonstrate that PassGIN consistently outperforms state-of-the-art models in both intra-site and cross-site password guessing scenarios, achieving relative improvements of 23.49% and 74.53%, respectively.

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

Searchable symmetric encryption has been vulnerable to inference attacks that rely on uniqueness in leakage patterns. However, many keywords in datasets lack distinctive leakage patterns, limiting the effectiveness of such attacks. The file injection attacks, initially proposed by Cash et al. (CCS 2015), have shown impressive performance with 100% accuracy and no prior knowledge requirement. Nevertheless, this attack fails to recover queries with underlying keywords not present in the injected files. To address these limitations, our research introduces a novel attack strategy called LEAP-Hierarchical Fusion Attack (LHFA) that combines the strengths of both file injection attacks and inference attacks. Before initiating keyword injection, we introduce a new approach for inert/active keyword selection. In the phase of selecting injected keywords, we focus on keywords without unique leakage patterns and recover them, leveraging their presence for document recovery. Our goal is to achieve an amplified effect in query recovery. We demonstrate a minimum query recovery rate of 1.3 queries per injected keyword with a 10% data leakage of a real-life dataset, and initiate further research to overcome challenges associated with non-distinctive keywords.

In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish to disclose their private data samples either. Conventional privacy-preserving deep learning solutions ask the users to send encrypted samples to the model owners, who must handle the heavy lifting of ciphertext-domain computation with homomorphic encryption. In this paper, we present a novel solution, namely, PrivDNN, which (1) offloads the computation to the user side by sharing an encrypted deep learning model with them, (2) significantly improves the efficiency of DNN evaluation using partial DNN encryption, (3) ensures model accuracy and model privacy using a core neuron selection and encryption scheme. Experimental results show that PrivDNN reduces privacy-preserving DNN inference time and memory requirement by up to 97% while maintaining model performance and privacy. Codes can be found at https://github.com/LiangqinRen/PrivDNN

Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation and efficiency of training. However, there is a fundamental lack of research on how to conduct VFL securely over distributed labels. This work is the first to fill this gap by designing a novel protocol, called FEVERLESS, based on XGBoost. FEVERLESS leverages secure aggregation via information masking technique and global differential privacy provided by a fairly and randomly selected noise leader to prevent private information from being leaked in the training process. Furthermore, it provides label and data privacy against honest-but-curious adversaries even in the case of collusion of$n - 2$out of n clients. We present a comprehensive security and efficiency analysis for our design, and the empirical results from our experiments demonstrate that FEVERLESS is fast and secure. In particular, it outperforms the solution based on additive homomorphic encryption in runtime cost and provides better accuracy than the local differential privacy approach.

Sidechains have been widely used to improve the interoperability and scalability of blockchain systems. Despite several interesting sidechain constructions have been proposed in the literature, they suffer from the following downsides: (1) their designs do not easily support pluggable consensus mechanisms, and (2) their communication and storage costs for cross-chain operations are not yet optimized. In this work, we first propose Ge-Co, a generic sidechain construction to realize secure asset transfers between blockchains, supporting different consensus algorithms, such as Proof-of-Stake (PoS) and Proof-of-Work (PoW). Our design is built on top of the proposed voting committee selection approach and threshold signature schemes (TSS) and meanwhile, it achieves optimally succinct and constant proof size, only yielding lightweight communication and storage costs. Ge-Co works in the semi-adaptive corruption model. To provide stronger security, we further propose PoS-Co, a PoS-based sidechain construction in the fully-adaptive corruption model. PoS-Co is based on the proposed anonymous committee selection approach, and preserves optimally succinct proof. We also formally prove that Ge-Co can achieve the security properties of atomicity and timeliness. Finally, we develop a proof-of-concept (PoC) implementation for Ge-Co, and the results demonstrate that the design is efficient and practical.

Modeling password distributions is a fundamental problem in password security, benefiting the research and applications on password guessing, password strength meters, honey password vaults, etc. As one of the best segment-based password models, WordPCFG has been proposed to capture individual semantic segments (called words) in passwords. However, we find WordPCFG does not address well the ambiguity of password segmentation by maximum matching, leading to the unreasonable segmentation of many password and further the inaccuracy of modeling password distributions. To address the ambiguity, we improve WordPCFG by maximum probability segmentation with A*-like pruning algorithm. The experimental results show that the improved WordPCFG cracks 99.26%–99.95% passwords, with nearly 5.67%–18.01% improvement.

Synthetic data generation plays a crucial role in many areas where data is scarce and privacy/confidentiality is a significant concern. Generative Adversarial Networks (GANs), arguably one of the most widely used data synthesis techniques, allow for the training of a model (i.e., generator) that can generate real-looking data by playing a min-max game with a discriminator model. When multiple organizations are reluctant to share their sensitive data, GANs models can be trained in a federated manner, commonly with the use of differential privacy (DP). In order to achieve a reasonable level of model utility, DP trades privacy exhibiting vulnerability to various attacks (e.g., membership inference attack). In this paper, we propose a hybrid solution, PP-FedGAN, to the asynchronous federated, privacy-preserving training of GANs models by combining the CKKS homomorphic encryption (HE) scheme with differential privacy. The addition of HE results in around 10 seconds of overhead on the client side per round and 115 seconds on the entire training procedure. We also analyze the security of PP-FedGAN under the honest-but-curious security model. Where stronger security guarantees are required, our proposal presents a better alternative to solutions that only employ DP.

Nowadays, Internet of Things applications face serious data and privacy protection vulnerabilities. To address some of the data protection and privacy issues, in this work we propose a new design for the self-encryption method based on a cryptographic-puzzle algorithm, that includes the generation of multiple secret keys, derived from the plaintext. As the ciphertext is constructed from several chunks of encrypted data, the absence of one of the decryption keys or one of the encrypted chunks renders recovery of the original plaintext nearly impossible. As security improvement upon to other related work proposing self-encryption, the plaintext is mixed with random values in order to use a technique known as Privacy Amplification. Privacy Amplification is achieved by applying cryptographic functions from which SHA-2 family is based on. Implementations of our design are also provided, and they are enabled for standalone and back-end execution systems. Furthermore, performance and security results and comparisons with previous related work are also provided. The security analysis confirms the use of the SHA-2 cryptographic hash family for Privacy Amplification.

Abstract Traffic encrypted technology enables Internet users to protect their data secrecy, but it also brings a challenge to malicious package detection. To tackle this issue, researchers have investigated into encrypted traffic analysis (ETA) in recent years. Existing works, however, only focus on the accuracy of malicious flow identification. Using ETA as a technical black box, they pay little attention to the internal details and explanation of models. In this paper, we, for the first time, introduce interpretable machine learning into ETA. We aim to provide a reasonable explanation for detection results, so as to enable one to understand and further trust network security analysts. We develop a complete analysis framework, named DEV-ETA (detection, explanation and verification of ETA). DEV-ETA applies post hoc interpretation methods to explain the detection results and verify the explanation using the joint distribution of support features on the dataset. We run thorough experiments to explain the detection result using three popular explanation approaches, namely SHAP, LIME and MSS, and we verify the explanation via the feature distribution plot. The experimental results show that our design can interpret the detection result of ETA model instead of just simply treating the model as a black box.

Genotype imputation estimates missing genotypes from the haplotype or genotype reference panel in individual genetic sequences, which boosts the potential of genome-wide association and is essential in genetic data analysis. However, the genetic sequences involve people’s privacy, confirming an individual’s identification and even disease information. This work proposes a secure genotype imputation model, which uses a linear regression model and the homomorphic encryption scheme over ciphertext to impute missing genotypes. The inference model is trained with float plaintext parameters, which are round into integers to avoid high complexity homomorphic evaluation on float number operations without bootstrapping operations. Even though the rounding parameters in the inference model are not the same as those in the trained model, We find that it will no effect on the outcome of the homomorphic prediction. Thus, a high-efficiency genotype imputation inference model over the ciphertext is obtained while keeping the high-security level. The simulation results indicate that the accuracy of the secure inference model is almost the same as the original model trained on float parameters. The secure inference model’s accuracy is 98.6% for a single genotype.

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. Moreover, the framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on such a matrix, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum and prevent the enlargement of error bounds. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experimental results indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

Existing proxy re-encryption (PRE) schemes to secure cloud data sharing raise challenges such as supporting the heterogeneous system efficiently and achieving the unbounded feature. To address this problem, we proposed a fast and secure unbounded cross-domain proxy re-encryption scheme, named FABRIC, which enables the delegator to authorize the semi-trusted cloud server to convert one ciphertext of an identity-based encryption (IBE) scheme to another ciphertext of an attribute-based encryption (ABE) scheme. As the first scheme to achieve the feature mentioned above, FABRIC not only enjoys constant computation overhead in the encryption, decryption, and re-encryption phases when the quantity of attributes increases, but is also unbounded such that the new attributes or roles could be adopted into the system anytime. Furthermore, FABRIC achieves adaptive security under the decisional linear assumption (DLIN). Eventually, detailed theoretical and experimental analysis proved that FABRIC enjoys excellent performance in efficiency and practicality in the cloud computing scenario.

Password-only authentication is one of the most popular secure mechanisms for real-world online applications. But it easily suffers from a practical threat - password leakage, incurred by external and internal attackers. The external attacker may compromise the password file stored on the authentication server, and the insider may deliberately steal the passwords or inadvertently leak the passwords. So far, there are two main techniques to address the leakage: Augmented password-authentication key exchange (aPAKE) against insiders and honeyword technique for external attackers. But none of them can resist both attacks. To fill the gap, we propose the notion of honey PAKE (HPAKE) that allows the authentication server to detect the password leakage and achieve the security beyond the traditional bound of aPAKE. Further, we build an HPAKE construction on the top of the honeyword mechanism, honey encryption, and OPAQUE which is a standardized aPAKE. We formally analyze the security of our design, achieving the insider resistance and the password breach detection. We implement our design and deploy it in the real environment. The experimental results show that our protocol only costs 71.27 ms for one complete run, within 20.67 ms on computation and 50.6 ms on communication. This means our design is secure and practical for real-world applications.

It has become a trend for clients to outsource their encrypted databases to remote servers and then leverage the Searchable Encryption technique to perform secure data retrieval. However, the method has yet to be considered a crucial need for replication on searchable encrypted data. It calls for challenging works on Dynamic Searchable Symmetric Encryption (DSSE) since clients must share the search capability of the encrypted data replicas and guarantee forward and backward privacy. We define a new notion called “Keyword Search Shareable Encryption” (KS2E) and the corresponding security model capturing forward and backward privacy. In our notion, data owners are allowed to share search indexes of the encrypted data with users. A search index will be updated with a new search key before sharing to guarantee the data privacy of the source database. The target database also inherits data search efficiency along with the shared data. We further construct an instance of KS2E calledBranch, prove its security, and use real-world datasets to evaluate Branch. The evaluation results show that Branch’s performance is comparable to classical DSSE schemes on search efficiency and demonstrate the effectiveness on searching encrypted data replicas from multiple owners.

Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due to their superior ability to learn representations for complex graph data. Due to privacy concerns and regulation restrictions, centralized GNNs can be difficult to apply to data-sensitive scenarios. Federated learning (FL) is an emerging technology developed for privacy-preserving settings when several parties need to train a shared global model collaboratively. Although several research works have applied FL to train GNNs (Federated GNNs), there is no research on their robustness to backdoor attacks.

Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern. Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model. These attacks are susceptible to backdoor detection algorithms and even visual inspection. In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms. We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can 1) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data.

Passwords have been widely used in online authentication, and they form the front line that protects our data security and privacy. But the security of password may be easily harmed by insecure password generator. Massive reports state that users are always keen to generate new passwords by reusing or fine-tuning old secrets. Once an old password is leaked, the users may suffer from credential tweaking attacks. We propose a password reuse model PassTrans and simulate credential tweaking attacks. We evaluate the performance in leaked password datasets, and the results show that 67.51% of accounts is breakable under 1,000 guesses, indicating our model is accurate in capturing password reuse behavior.

To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on "long" passwords, indicating the importance of semantic parts in long passwords.

Abstract A redactable consortium blockchain (RCB) can build a trust layer for industrial internet of things (IIoT) so as to enable IIoT to resist certain powerful attacks resulting in improper block content. The redactability is particularly important for blockchains applied in IIoT with valuable or sensitive activities such as financial IoT or energy-trading IoT. Huang et al. proposed a threshold chameleon hash (TCH) scheme and then constructed an accountable-and-sanitizable chameleon signature scheme based on TCH. These two primitives are further used as fundamental modules to build an RCB, which empowers IIoT devices to operate the blockchain in a controllable way. However, our paper shows that Huang et al.’s RCB suffers from a security problem that weakens the crucial redactability. Specifically, we find out that if a transaction in a given block is legally redacted by all authorized sensors who collectively hold the private redacting key, anyone (without any private information) can further redact this redacted transaction and delete any transaction within this redacted block and, meanwhile, any sensor user with a private signing (not redacting) key can insert a forged transaction into this redacted block. We further address this threat by replacing the TCH module in Huang et al.’s RCB with our designed TCH.

Cloud-based data storage service has drawn increasing interests from both academic and industry in the recent years due to its efficient and low cost management. Since it provides services in an open network, it is urgent for service providers to make use of secure data storage and sharing mechanism to ensure data confidentiality and service user privacy. To protect sensitive data from being compromised, the most widely used method is encryption. However, simply encrypting data (e.g., via AES) cannot fully address the practical need of data management. Besides, an effective access control over download request also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service. In this article, we consider thedual access control, in the context of cloud-based storage, in the sense that we design a control mechanism over both data access and download request without loss of security and efficiency. Two dual access control systems are designed in this article, where each of them is for a distinct designed setting. The security and experimental analysis for the systems are also presented.

An important motivation for research in location privacy has been to protect against user profiling, i.e., inferring a user’s political affiliation, wealth level, sexual preferences, religious beliefs, and other sensitive attributes. Existing approaches focus on distorting or suppressing individual locations, but we argue that, for directly protecting against profiling, it is more appropriate to focus on the frequency with which various locations are visited – in other words, the histogram of a user’s locations. We introduce and explore a new privacy notion, namely, on-the-fly privacy for location histograms, in which a mobile user repeatedly submits obfuscated locations to a Location-Based Service aiming for the resulting histogram to resemble a target profile or differ from it. For example, she may want to avoid looking wealthy or to resemble a health-conscious person. We describe how to design concrete privacy mechanisms that operate under different assumptions on, e.g., the user’s mobility, including provably optimal mechanisms. We use a mobility dataset with 1083 users to illustrate how these mechanisms achieve privacy while minimizing the quality loss caused by the location obfuscation, in the context of two types of Location-Based Services: nearest-PoI, and geofence.

To mitigate cross-site scripting attacks (XSS), the W3C group recommends web service providers to employ a computer security standard called Content Security Policy (CSP). However, less than 3.7 percent of real-world websites are equipped with CSP according to Google’s survey. The low scalability of CSP is incurred by the difficulty of deployment and non-compatibility for state-of-art browsers. To explore the scalability of CSP, in this article, we propose JavaScript based CSP (JSCSP), which is able to support most of real-world browsers but also to generate security policies automatically. Specifically, JSCSP offers a novel self-defined security policy which enforces essential confinements to related items, including JavaScript functions, DOM elements and data access. Meanwhile, JSCSP has an efficient algorithm to automatically generate the policy directives and enforce them in a cascading way, which is more fine-grained and practical than the functionalities provided by CSP. We further implement JSCSP on a Chrome extension, and our evaluation shows that the extension is compatible with popular JavaScript libraries. Our JSCSP extension can detect and block the tested attacking vectors extracted from the prevalent web applications. We state that JSCSP delivers better performance compared to other XSS defense solutions.

Dynamic searchable symmetric encryption (DSSE) has been widely recognized as a promising technique to delegateupdateandsearchqueries over an outsourced database to an untrusted server while guaranteeing the privacy of data. Many efforts on DSSE have been devoted to obtaining a good tradeoff between security and performance. However, it appears that all existing DSSE works miss studying on what will happen if the DSSE client issues irrationalupdatequeries carelessly, such as duplicateupdatequeries anddeletequeries to remove non-existent entries (that have been considered by many popular database system in the setting of plaintext). In this scenario, we find that (1) most prior works lose their claimed correctness or security, and (2) no single approach can achieve correctness, forward and backward security, and practical performance at the same time. To address this problem, we study for the first time the notion of robustness of DSSE. Generally, we say that a DSSE scheme is robust if it can keep the same correctness and security even in the case of misoperations. Then, we introduce a new cryptographic primitive named key-updatable pseudo-random function and apply this primitive to constructing ROSE, a robust DSSE scheme with forward and backward security. Finally, we demonstrate the efficiency of ROSE and give the experimental comparisons.

Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Lianget al.proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency,$O(n d)$, where$d$is the total number of system files and$n$is the size of query formula. This article, for the first time, leverages the “oblivious cross search” technology in public key searchable encryption context to reduce the search complexity toonly$O(nf(w))$, where$f(w)$is the number of files embedded with the “least frequent keyword”$w$. The new scheme maintains efficient encrypted data share and keyword field update as well. This article further revisits the security models for payload security, keyword privacy and search token privacy (i.e., search pattern privacy) and meanwhile, presents security and efficiency analysis for the new scheme.

Probabilistic context-free grammars (PCFGs) have been pro-posed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to misestimation of password probability and thus seriously affects their performance. In this paper, we propose a word extraction approach for passwords, and further present an improved PCFG model, called WordPCFG. The WordPCFG using word extraction method can precisely extract semantic segments (called word) from passwords based on cohesion and freedom of words. We evaluate our WordPCFG on six large-scale datasets, showing that WordPCFG cracks 83.04%–95.47% passwords and obtains 12.96%–71.84% improvement over the state-of-the-art PCFGs.

Multi-factor authentication (MFA) has been widely used to safeguard high-value assets. Unlike single-factor authentication (e.g., password-only login), t-factor authentication ( tFA) requires a user always to carry and present t specified factors so as to strengthen the security of login. Nevertheless, this may restrict user experience in limiting the flexibility of factor usage, e.g., the user may prefer to choose any factors at hand for login authentication. To bring back usability and flexibility without loss of security, we introduce a new notion of authentication, called (t,n) threshold MFA, that allows a user to actively choose t factors out of n based on preference. We further define the “most-rigorous” multi-factor security model for the new notion, allowing attackers to control public channels, launch active/passive attacks, and compromise/corrupt any subset of parties as well as factors. We state that the model can capture the most practical security needs in the literature. We design a threshold MFA key exchange (T-MFAKE) protocol built on the top of a threshold oblivious pseudorandom function and an authenticated key exchange protocol. Our protocol achieves the “highest-attainable” security against all attacking attempts in the context of parties/factors being compromised/corrupted. As for efficiency, our design only requires 4+t exponentiations, 2 multi-exponentiations and2communication rounds. Compared with existing tFA schemes, even the degenerated (t,t) version of our protocol achieves the strongest security (stronger than most schemes) and higher efficiency on computational and communication. We instantiate our design on real-world platform to highlight its practicability and efficiency.

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.

Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary pre-steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under various encryption mechanisms when data owners try to secure the confidentiality of the data. This makes data aggregation extremely challenging, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching service that securely bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data, i.e., after encryption switching one may search over the (outsourced) data without loss of data and query secrecy. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme may be applicable in multi-domain cloud storage system.

Secure cloud storage, which is an emerging cloud service, is designed to protect the confidentiality of outsourced data but also to provide flexible data access for cloud users whose data is out of physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising techniques that may be leveraged to secure the guarantee of the service. However, the use of CP-ABE may yield an inevitable security breach which is known as the misuse of access credential (i.e., decryption rights), due to the intrinsic “all-or-nothing” decryption feature of CP-ABE. In this paper, we investigate the two main cases of access credential misuse: one is on the semi-trusted authority side, and the other is on the side of cloud user. To mitigate the misuse, we propose the first accountable authority and revocable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud±. We also present the security analysis and further demonstrate the utility of our system via experiments.

Abstract To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation.

Abstract Recently, Tseng et al. proposed a new notion for identity-based signature (IBS) scheme to resist ephemeral secret leakage (ESL) attacks, called leakage-free identity-based signature (leakage-free IBS), and devised the first secure leakage-free IBS scheme. However, they only considered the situation of the leakage of ephemeral secrets used for generating the signatures. Notice that the private key extraction procedure is probabilistic as well in their scheme, that is, there are ephemeral secrets used by the key generation center to generate the signers’ private keys. It is practical to consider that if the adversary comprises these ephemeral secrets, then he can reveal the master key of the system. Therefore, it is desired to introduce a new security notion for the leakage-free IBS schemes to consider the ESL attacks on both private key extraction and signing procedures. In this paper, we present such security notion. Moreover, we propose two IBS schemes that are proved to be secure under the new security notion.

Android Apps have become the most important mobile applications in the evolving mobile IoT systems, whose security and privacy are confronted with ever more challenges, since such mobile devices as smartphones involve too much personal privacy information. Meanwhile, the developers prefer to put core functions (e.g., encryption function and T9 search function) of Android applications in the native layer for execution efficiency. However, there are no automated security analysis tools to protect the security and privacy of the Android native layer, especially for those dynamically loaded third-party SO libraries. In order to solve the previous problem, which is confusing, we propose a novel and scalable system, called SoProtector, to prevent privacy from leaking via the analysis of data flow between the Java and native layers. For detection of the malicious function implanted in the SO libraries, SoProtector realizes a real-time engine. We derive the malware features via three steps: 1) present binary files in native family as a grayscale image; 2) with use of the ARM instructions set reversely obtain the code of the SO file and using Python to obtain the opcode sequence; and 3) each file is transformed as the form of assembly language by IDA Pro, which includes a gdl file as an accompaniment. Our experiment, which involved 3400 applications, demonstrates that SoProtector is able to detect more sinks, sources, and smudges. It effectively inspects and blocks at least 82% of the applications that are loading malicious third-party SO dynamically, and it has relatively low overhead in the meantime, compared to most of the existing static analysis tools (e.g., FlowDroid and AndroidLeaks).

If all vehicles are connected together through a wireless communication channel, vehicular ad hoc networks (VANETs) can support a wide range of real-time traffic information services, such as intelligent routing, weather monitoring, emergency call, etc. However, the accuracy and credibility of the transmitted messages among the VANETs are of paramount importance as life may depend on it. In this article we introduce a novel framework called blockchain-assisted privacy-preserving authentication system (BPAS) that provides authentication automatically in VANETs and preserves vehicle privacy at the same time. This design is highly efficient and scalable. It does not require any online registration centre (except for system initialization and vehicle registration), and allows conditional tracing and dynamic revocation of misbehaving vehicles. In this article, we conduct an in-depth security analysis and a comprehensive performance evaluation (which is based on the Hyperledger Fabric platform) for our proposed framework. The results demonstrate that our framework is an efficient solution for the development of a decentralized authentication system in VANETs.

Artificial Intelligence (AI) based technologies have deeply changed people's daily lives. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Although bringing AI into industrial scenarios could improve the performance in many aspects, new security and privacy issues are also introduced consequently. Subsequently, machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. As many machine learning and deep learning models are vulnerable against well-designed adversarial input samples, outsourcing data and algorithms for training will require the integrity of the training data. Also, data privacy of the end users must be protected. On the other hand, traditional solutions for industrial system security could also be enhanced by these AI schemes. The papers in this special section focus on emerging privacy and security issues brought by Artificial Intelligence in industrial informatics.

The Internet of Things (IoT) is rapidly evolving, while introducing several new challenges regarding security, resilience and operational assurance. In the face of an increasing attack landscape, it is necessary to cater for the provision of efficient mechanisms to collectively verify software- and device-integrity in order to detect run-time modifications. Towards this direction, remote attestation has been proposed as a promising defense mechanism. It allows a third party, the verifier, to ensure the integrity of a remote device, the prover. However, this family of solutions do not capture the real-time requirements of industrial IoT applications and suffer from scalability and efficiency issues. In this paper, we present a lightweight dynamic control-flow property-based attestation architecture (CFPA) that can be applied on both resource-constrained edge and cloud devices and services. It is a first step towards a new line of security mechanisms that enables the provision of control-flow attestation of only those specific, critical software components that are comparatively small, simple and limited in function, thus, allowing for a much more efficient verification. Our goal is to enhance run-time software integrity and trustworthiness with a scalable and decentralized solution eliminating the need for federated infrastructure trust. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security do not hinder the deployment of intelligent edge computing systems.

Abstract Online data sharing has become a research hotspot while cloud computing is getting more and more popular. As a promising encryption technique to guarantee the security shared data and to realize flexible fine-grained access control, ciphertext-policy attribute-based encryption (CP-ABE) has drawn wide attentions. However, there is a drawback preventing CP-ABE from being applied to cloud applications. In CP-ABE, the access structure is included in the ciphertext, and it may disclose user’s privacy. In this paper, we find a more efficient method to connect ABE with inner product encryption and adopt several techniques to ensure the expressiveness of access structure, the efficiency and security of our scheme. We are the first to present a secure, efficient fine-grained access control scheme with hidden access structure, the access structure can be expressed as AND-gates on multi-valued attributes with wildcard. We conceal the entire attribute instead of only its values in the access structure. Besides, our scheme has obvious advantages in efficiency compared with related schemes. Our scheme can make data sharing secure and efficient, which can be verified from the analysis of security and performance.

With the advancement of Internet of Things (IoT), a large number of electronic devices are connected to the Internet. These connected electronic devices acquire and transmit information, and respond to any received actions. In the medical ecosystem, hospitals can implement medical diagnosis (MD) with medical sensors, especially for remote auxiliary MD. But, in this context, patients' privacy (PP) is of paramount importance, and confidentiality of medical data is crucial. Therefore, the main challenge ahead is how to realize remote auxiliary MD while protecting confidentiality of the medical data and ensuring PP. In this article, based on somewhat homomorphic encryption (SHE) scheme addressed by Junfeng Fan and Frederik Vercauteren (FV), we provide the first instance of a new efficient SHE scheme for homomorphic evaluation over single instruction multiple data (SIMD). We also implement a new set of efficient SIMD homomorphic comparison and division schemes. Based on these findings, we implement efficient privacy preserving and SIMD homomorphic surf and multiretina-image matching schemes. Offered functionalities include SIMD homomorphic feature point detection, multiretina-image matching, and lesion detection for the encrypted retinal image of diabetic retinopathy. Finally, we provide a proof-of-concept application implementation toward remote auxiliary diagnosis systems for diabetes in order to showcase the core security and privacy pillars of our solution. In the meantime, our IoT system designed with lattice-based cryptography preserves data confidentiality under quantum computation and quantum computers.

In leakage resilient cryptography, there is a seemingly inherent restraint on the ability of the adversary that it cannot get access to the leakage oracle after the challenge. Recently, a series of works made a breakthrough to consider a postchallenge leakage. They presented achievable public key encryption (PKE) schemes which are semantically secure against after-the-fact leakage in the split-state model. This model puts a more acceptable constraint on adversary’s ability that the adversary cannot query the leakage of secret states as a whole but the functions of several parts separately instead of prechallenge query only. To obtain security against chosen ciphertext attack (CCA) for PKE schemes against after-the-fact leakage attack (AFL), existing works followed the paradigm of “double encryption” which needs noninteractive zero knowledge (NIZK) proofs in the encryption algorithm. We present an alternative way to achieve AFL-CCA security via lossy trapdoor functions (LTFs) without NIZK proofs. First, we formalize the definition of LTFs secure against AFL (AFLR-LTFs) and all-but-one variants (ABO). Then, we show how to realize this primitive in the split-state model. This primitive can be used to construct AFLR-CCA secure PKE scheme in the same way as the method of “CCA from LTFs” in traditional sense.

Searchable encryption (SE) provides a privacy-preserving mechanism for data users to search over encrypted data stored on a remote server. Researchers have designed a number of SE schemes with high efficiency yet allowing some degree of leakage profile to the remote server. The leakage, however, should be further measured to allow us to understand what types of attacks an SE scheme would encounter. This paper considers passive attacks that make inferences based on prior knowledge and observations on queries issued by users. This is in contrast to previously studied active attacks that adaptively inject files and queries. We consider several assumptions on the types or prior knowledge the attacker possessed and propose a few passive attacks. In particular, under the “full-fledged” assumption, the keyword recovery rate of our attack is optimal in the sense that it is equal to the theoretical upper bound. We further present several enhanced attacks under other weaker assumptions on various levels of the prior knowledge that the attacker can obtain, in which the keyword recovery rates are optimal or nearly optimal (i.e., approaching the theoretical upper bound). In addition, we provide extensive experiments to show the “power” of our passive attacks. This paper highlights the importance of minimizing the prior knowledge of a server and the leakage of search queries. It also shows that simply distorting the frequency of the keyword to hold against our passive attacks may not scale well.

In recent years, wireless sensor networks (WSNs) have become an active area of research for monitoring physical and environmental conditions. Due to the interdependence of sensors, a functional anomaly in one sensor can cause a functional anomaly in another sensor, which can further lead to the malfunctioning of the entire sensor network. Existing research work has analysed faulty sensor anomalies but fails to show the effectiveness throughout the entire interdependent network system. In this article, a dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning. Through experiment on a specific thermal power plant in China, we verify the robustness of our proposed method in detecting abnormal nodes against four state of the art approaches and proved our method is more robust. Furthermore, the experiments are conducted on the obtained abnormal nodes to prove the interdependence of multi-layer sensor networks and reveal the conditions and causes of a system crash.

As a sophisticated mechanism for secure finegrained access control over encrypted data, ciphertext-policy attribute-based encryption (CP-ABE) is one of the highly promising candidates for cloud computing applications. However, there exist two main long-lasting open problems of CP-ABE that may limit its wide deployment in commercial applications. One is that decryption yields expensive pairing cost which often grows with the increase of access policy size. The other is that one is granted access privilege for unlimited times as long as his attribute set satisfies the access policy of a given ciphertext. Such powerful access rights, which are provided by CP-ABE, may be undesirable in real-world applications (e.g., pay-as-youuse). To address the above drawbacks, in this paper, we propose a new notion called auditable σ-time outsourced CF-ABE, which is believed to be applicable to cloud computing. In our notion, expensive pairing operation incurred by decryption is offloaded to cloud and meanwhile, the correctness of the operation can be audited efficiently. Moreover, the notion provides σ-time fine-grained access control. The cloud service provider may limit a particular set of users to enjoy access privilege for at most σ times within a specified period. As of independent interest, the notion also captures key-leakage resistance. The leakage of a user's decryption key does not help a malicious third party in decrypting the ciphertexts belonging to the user. We design a concrete construction (satisfying our notion) in the key encapsulation mechanism setting based on Rouselakis and Waters (prime order) CP-ABE, and further present security and extensive experimental analysis to highlight the scalability and efficiency of our construction.

Industrial Internet of Things (IIoT) integrates various types of intelligent terminals, mobile devices, and communication technologies to enable the upgrade of traditional industries to intelligent industries. IIoT relies on the powerful data processing capabilities of cloud computing to reduce the cost of various on-demand services as per the requirements of users. However, the privacy and confidentiality of the outsourced data should be protected in this environment because the data are typically “handled” by a third-party service provider. An encryption technique can guarantee the confidentiality of the data but it limits data retrieval due to its innate “all-or-nothing” decryption feature. To apply encryption to privacy-preserving data retrieval, many public key encryption techniques with keyword search systems have been proposed in the literature. However, most of the existing schemes are vulnerable to inside keyword guessing attack (IKGA), which is caused by a small keyword space. To address this problem, we propose a certificateless public key authenticated encryption with keyword search scheme, which is provably secure against IKGA. A performance analysis of the proposed scheme demonstrates that it is more secure and effective compared with other certificateless public key encryption with keyword search schemes.

Data outsourcing is a promising service for data owners, where their data are stored on a cloud storage provider. Since the cloud is not fully trusted, data access control has become a challenging issue in the Cloud Storage System (CSS). Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a feasible technique for ensuring access control in the CSS, where an attribute authority is responsible to manage attributes and distribute keys. In this paper, we propose a novel revocable Multi-Authority CP-ABE scheme, in which the access policy can be constructed as an arbitrary tree rather than a matrix used by existing schemes. The tree-like policy makes our scheme more flexible. Consequently, the encryption, decryption and attribute revocation operations are also more efficient. Our scheme is also proved to be secure under the standard assumption. It can resist user collusion attack, while the attribute revocation operation also achieves both forward security and backward security. Simulation results show that our scheme is highly efficient.

Outsourcing a huge amount of local data to remote cloud servers that has been become a significant trend for industries. Leveraging the considerable cloud storage space, industries can also put forward the outsourced data to cloud computing. How to collect the data for computing without loss of privacy and confidentiality is one of the crucial security problems. Searchable encryption technique has been proposed to protect the confidentiality of the outsourced data and the privacy of the corresponding data query. This technique, however, only supporting search functionality, may not be fully applicable to real-world cloud computing scenario whereby secure data search, share as well as computation are needed. This work presents a novel encrypted cloud-based data share and search system without loss of user privacy and data confidentiality. The new system enables users to make conjunctive keyword query over encrypted data, but also allows encrypted data to be efficiently and multiply shared among different users without the need of the "download-decrypt-then-encrypt" mode. As of independent interest, our system provides secure keyword update, so that users can freely and securely update data's keyword field. It is worth mentioning that all the above functionalities do not incur any expansion of ciphertext size, namely, the size of ciphertext remains constant during being searched, shared and keyword-updated. The system is proven secure and meanwhile, the efficiency analysis shows its great potential in being used in large-scale database.

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

Anonymous proxy signatures are very useful in the construction of anonymous credential systems such as anonymous voting and anonymous authentication protocols. As a basic requirement, we should ensure an honest proxy signer is anonymous. However, in order to prevent the proxy signer from abusing the signing right, we should also allow dishonest signers to be traced. In this paper, we present three novel anonymous proxy signature schemes with different levels of (namely, public, internal and original signer) traceability. We define the formal definitions and security models for these three different settings, and prove the security of our proposed schemes under some standard assumptions.

In this paper, we propose a two-factor data security protection mechanism with factor revocability for cloud storage system. Our system allows a sender to send an encrypted message to a receiver through a cloud storage server. The sender only needs to know the identity of the receiver but no other information (such as its public key or its certificate). The receiver needs to possess two things in order to decrypt the ciphertext. The first thing is his/her secret key stored in the computer. The second thing is a unique personal security device which connects to the computer. It is impossible to decrypt the ciphertext without either piece. More importantly, once the security device is stolen or lost, this device is revoked. It cannot be used to decrypt any ciphertext. This can be done by the cloud server which will immediately execute some algorithms to change the existing ciphertext to be un-decryptable by this device. This process is completely transparent to the sender. Furthermore, the cloud server cannot decrypt any ciphertext at any time. The security and efficiency analysis show that our system is not only secure but also practical.

Using cloud-based storage service, users can remotely store their data to clouds but also enjoy the high quality data retrieval services, without the tedious and cumbersome local data storage and maintenance. However, the sole storage service cannot satisfy all desirable requirements of users. Over the last decade, privacy-preserving search over encrypted cloud data has been a meaningful and practical research topic for outsourced data security. The fact of remote cloud storage service that users cannot have full physical possession of their data makes the privacy data search a formidable mission. A naive solution is to delegate a trusted party to access the stored data and fulfill a search task. This, nevertheless, does not scale well in practice as the fully data access may easily yield harm for user privacy. To securely introduce an effective solution, we should guarantee the privacy of search contents, i.e., what a user wants to search, and return results, i.e., what a server returns to the user. Furthermore, we also need to guarantee privacy for the outsourced data, and bring no additional local search burden to user. In this paper, we design a novel privacy-preserving functional encryption-based search mechanism over encrypted cloud data. A major advantage of our new primitive compared with the existing public key based search systems is that it supports an extreme expressive search mode, regular language search. Our security and performance analysis show that the proposed system is provably secure and more efficient than some searchable systems with high expressiveness.

Ciphertext-policy attribute-based encryption (CP-ABE) is a very promising encryption technique for secure data sharing in the context of cloud computing. Data owner is allowed to fully control the access policy associated with his data which to be shared. However, CP-ABE is limited to a potential security risk that is known as key escrow problem, whereby the secret keys of users have to be issued by a trusted key authority. Besides, most of the existing CP-ABE schemes cannot support attribute with arbitrary state. In this paper, we revisit attribute-based data sharing scheme in order to solve the key escrow issue but also improve the expressiveness of attribute, so that the resulting scheme is more friendly to cloud computing applications. We propose an improved two-party key issuing protocol that can guarantee that neither key authority nor cloud service provider can compromise the whole secret key of a user individually. Moreover, we introduce the concept of attribute with weight, being provided to enhance the expression of attribute, which can not only extend the expression from binary to arbitrary state, but also lighten the complexity of access policy. Therefore, both storage cost and encryption complexity for a ciphertext are relieved. The performance analysis and the security proof show that the proposed scheme is able to achieve efficient and secure data sharing in cloud computing.

A proxy re-encryption (PRE) allows a data owner to delegate the decryption rights of some encrypted data stored on the cloud without revealing the data to an honest-but-curious cloud service provider (i.e. the PRE proxy). Furthermore, the data owner can offload most of the computational operations to the cloud service provider and hence, using PRE for encrypted cloud data sharing can be very effective even for data owners using limited resource devices (e.g. mobile devices). However, PRE schemes only enables data owners to delegate the decryption rights of all their encrypted data. A more practical notion is a conditional PRE (CPRE) that allows us to specify under what condition the decryption of an encrypted data can be delegated, for example, only sharing all the encrypted files under a directory called ‘public’. In this paper, we provide an affirmative result on the long-standing question of building a full chosen-ciphertext attacks (CCA)-secure CPRE system in the standard model and for the first time, we show that a class of Hierarchical Identity-Based Encryption (HIBE) schemes can be transferred to building a CCA-secure CPRE in the standard model. We also list out some concrete HIBE schemes which fall into this class, e.g., Lewko-Waters HIBE. All existing CCA-secure PRE schemes in the standard model are not conditional while all existing CPRE schemes are either not CCA secure or not in the standard model. By instantiating our generic HIBE-based transformation, we show that an efficient and concrete CPRE scheme which is both CCA secure in the standard model and conditional can be built.

SUMMARY Ciphertext‐policy attribute‐based proxy re‐encryption (CP‐ABPRE) extends the traditional Proxy Re‐Encryption (PRE) by allowing a semi‐trusted proxy to transform a ciphertext under an access policy to another ciphertext with the same plaintext under a new access policy (i.e., attribute‐based re‐encryption). The proxy, however, learns nothing about the underlying plaintext. CP‐ABPRE has many real world applications, such as fine‐grained access control in cloud storage systems and medical records sharing among different hospitals. All the existing CP‐ABPRE schemes are leaving chosen‐ciphertext attack (CCA) security as an interesting open problem. This paper, for the first time, proposes a new CP‐ABPRE scheme to tackle the problem. The new scheme supports attribute‐based re‐encryption with any monotonic access structures. Despite being constructed in the random oracle model, our scheme can be proven CCA secure under the decisional q‐parallel bilinear Diffie–Hellman exponent assumption. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract The privacy model of radio frequency identification (RFID) systems is for formalizing the adversarial capabilities and the security requirements of RFID anonymity and untraceability. Existing unpredictability‐based privacy models such as unp‐privacy, eunp‐privacy, unp*‐privacy, and unpτ‐privacy have captured different kinds of practical attacks, and some of them also have mutual authentication included. However, forward privacy, which allows a tag to remain untraceable even after its corruption, is yet to be well captured in any unpredictability‐based privacy models. In this paper, we describe some forward privacy‐related attacks that can be launched against RFID tags in practice. We then propose a new unpredictability‐based forward privacy model called unpfτ‐privacy. It extends an existing one called unpτ‐privacy, which has been shown to be stronger than ind‐privacy, unp‐privacy, and unp*‐privacy. We also propose an RFID protocol that supports forward privacy and mutual authentication. We show that it can be proven secure in the unpfτ‐privacy model. Copyright © 2015 John Wiley & Sons, Ltd.

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

To date, the growth of electronic personal data leads to a trend that data owners prefer to remotely outsource their data to clouds for the enjoyment of the high-quality retrieval and storage service without worrying the burden of local data management and maintenance. However, secure share and search for the outsourced data is a formidable task, which may easily incur the leakage of sensitive personal information. Efficient data sharing and searching with security is of critical importance. This paper, for the first time, proposes a searchable attribute-based proxy reencryption system. When compared with the existing systems only supporting either searchable attribute-based functionality or attribute-based proxy reencryption, our new primitive supports both abilities and provides flexible keyword update service. In particular, the system enables a data owner to efficiently share his data to a specified group of users matching a sharing policy and meanwhile, the data will maintain its searchable property but also the corresponding search keyword(s) can be updated after the data sharing. The new mechanism is applicable to many real-world applications, such as electronic health record systems. It is also proved chosen ciphertext secure in the random oracle model.

The need of secure big data storage service is more desirable than ever to date. The basic requirement of the service is to guarantee the confidentiality of the data. However, the anonymity of the service clients, one of the most essential aspects of privacy, should be considered simultaneously. Moreover, the service also should provide practical and fine-grained encrypted data sharing such that a data owner is allowed to share a ciphertext of data among others under some specified conditions. This paper, for the first time, proposes a privacy-preserving ciphertext multi-sharing mechanism to achieve the above properties. It combines the merits of proxy re-encryption with anonymous technique in which a ciphertext can be securely and conditionally shared multiple times without leaking both the knowledge of underlying message and the identity information of ciphertext senders/recipients. Furthermore, this paper shows that the new primitive is secure against chosen-ciphertext attacks in the standard model.

In this paper, for the first time, we define a general notion for proxy re-encryption (PRE), which we call deterministic finite automata-based functional PRE (DFA-based FPRE). Meanwhile, we propose the first and concrete DFA-based FPRE system, which adapts to our new notion. In our scheme, a message is encrypted in a ciphertext associated with an arbitrary length index string, and a decryptor is legitimate if and only if a DFA associated with his/her secret key accepts the string. Furthermore, the above encryption is allowed to be transformed to another ciphertext associated with a new string by a semitrusted proxy to whom a re-encryption key is given. Nevertheless, the proxy cannot gain access to the underlying plaintext. This new primitive can increase the flexibility of users to delegate their decryption rights to others. We also prove it as fully chosen-ciphertext secure in the standard model.