With the widespread application of Internet of Things (IoT) technology in the medical field, real-time collection and transmission of medical images become feasible. However, existing privacy-preserving image retrieval schemes often suffer from low efficiency and high communication overhead when operated in resource-constrained IoT environments due to the lack of efficient models. Thus, achieving efficient and secure medicalimage retrieval on limited-resource devices has emerged as a critical challenge. To address this, we propose LPPUBR, a lightweight, privacy-preserving, unsupervised bitmap retrieval scheme designed for IoT environments with constrained resources. LPPUBR utilizes a secure and lightweight deep learning model to extract deep feature descriptors from images and employs product quantization (PQ) to encode them into binary bitmaps, enhancing retrieval efficiency while reducing computational and storage costs. Particularly, a cross-quantization contrastive learning strategy is applied to jointly train the neural network model and PQ codewords for unsupervised learning. Furthermore, to improve interaction efficiency and reduce communication costs among multiple servers, we optimize the intermediate value recovery operation and redesign the related protocols in n-party secret sharing using a group communication strategy. A comprehensive theoretical analysis and experimental evaluation demonstrate that LPPUBR maintains retrieval accuracy comparable to the original unsupervised model while ensuring data security. Moreover, LPPUBR surpasses existing schemes in terms of computational cost, communication overhead, and retrieval efficiency.

Spatial crowdsourcing can quickly assign tasks and obtain feedback based on task requirements and workers’ locations, which brings great convenience to task assignment. However, sensitive information can also be easily obtained by spatial crowdsourcing platforms. To prevent information leakage, various privacy-preserving task assignment schemes have been proposed. However, existing schemes have low query efficiency and may leak pattern privacy, task content, or worker preference. To address the above challenges, this paper proposes a differentiated privacy-preserving task assignment scheme based on generative adversarial networks in spatial crowdsourcing–DPGAN-SC. This scheme leverages generative adversarial networks to generate disguised locations for both tasks and workers, which are then used in the task-matching process. Within the standard area range, no location can be distinguished, ensuring location privacy while preventing adversaries from analyzing search patterns through matching results. The combination of location disguise and task content encryption makes it impossible for adversaries to infer worker preferences and access patterns through the matching process. In addition, to meet differentiated privacy requirements, DPGAN-SC leverages generative adversarial networks to design a three-level privacy-classification mechanism. This mechanism categorizes private data while minimizing unnecessary privacy overhead. Compared to existing schemes, DPGAN-SC improves query efficiency by 100 times while ensuring comprehensive privacy preservation.

Existing schemes that combine provable data possession (PDP) and proof of ownership (PoW) for data integrity and efficient deduplication face several practical shortcomings. First, integrity tag generation relies on data block indices or user keys, causing redundant tags for duplicate data and increasing storage overhead. Second, dynamic operations like data insertion and deletion require linear scanning, resulting in a high computational complexity of$O(N)$. Furthermore, in multi-cloud backup environments, existing mechanisms struggle to ensure consistency between data ciphertexts and their tags. If an audit fails, locating damaged replicas requires repetitive verification by the third-party auditor (TPA), leading to high inefficiency. To overcome these issues, this paper proposes a multi-cloud data auditing mechanism (MDAM), which is based on an authenticated data structure called variable merkle hash tree (VMHT). MDAM utilizes message-derived RSA tags and secret-sharing-based proxy re-signing to achieve efficient deduplication and secure user-tag association. By integrating updatable block-level message-locked encryption (UMLE) technology with a variable branching structure, it supports dynamic updates with an$O(\log N)$complexity, saving storage and improving update efficiency. Additionally, MDAM employs a dynamic Bloom filter for efficient fault localization. Experimental results demonstrate that MDAM surpasses existing schemes in computational overhead, update efficiency, and fault localization performance.

With the rapid development of edge computing and the explosive growth of image data generated by IoT and mobile devices, an increasing number of users prefer to perform privacy-preserving image storage and retrieval tasks directly at the edge. However, existing solutions typically rely on basic encryption methods and shallow feature extraction, leading to inadequate data security and poor retrieval performance. In this paper, we propose a Dynamic Multi-Stage Encryption (DMSE) method combined with a semantically rich fusion feature to achieve high-precision and privacy-preserving image retrieval in edge environments. Specifically, the proposed method first divides the image into blocks and applies random shuffling, followed by channel and pixel-level XOR encryption to generate a hybrid encrypted image. Then, we extract global features from the encrypted image using the histogram of Discrete Cosine Transform (DCT) coefficients. In addition, a multi-scale convolution block is designed to extract stable and robust local features under encryption. Finally, deep learning is utilized to fuse the global and local features, capturing both the holistic structure and fine-grained semantics of the image. This comprehensive feature representation significantly improves retrieval accuracy while ensuring privacy. Extensive experiments validate that our approach outperforms existing methods in both security and retrieval effectiveness, making it well-suited for edge computing scenarios with limited resources and high privacy demands.

Detecting packed executables is a critical component of large-scale malware analysis and antivirus engine workflows, as it identifies samples that warrant computationally intensive dynamic unpacking to reveal concealed malicious behavior. Traditionally, packer detection techniques have relied on empirical features, such as high entropy or specific binary patterns. However, these empirical, feature-based methods are increasingly vulnerable to evasion by adversarial samples or unknown packers (e.g., low-entropy packers). Furthermore, the dependence on expert-crafted features poses challenges in sustaining and evolving these methods over time.

Assembly code analysis and comprehension play critical roles in applications like reverse engineering, yet they face substantial challenges due to low information density and a lack of explicit syntactic structures. While traditional masked language modeling (MLM) approaches do not explicitly focus on natural language interaction, emerging decoder-focused large language models (LLMs) demonstrate partial success in binary analysis yet remain underexplored for holistic comprehension. We present Assembly Augmented Tuning (ASMA-Tune), an end-to-end structural-semantic instruction tuning framework that synergizes encoder architecture with decoder-based LLMs through a projector module, where the assembly encoder extracts hardware-level structural features, the projector bridges representations with the semantic space, and the instruction-tuned LLM preserves natural language capabilities. Experimental results demonstrate three key advantages: (1) State-of-the-art performance in assembly comprehension with +39.7% Recall@1 and +17.8% MRR improvements over GPT-4-Turbo, (2) Consistent enhancements across base models (24.6–107.4% Recall@1 and 15.2–106.3% MRR on Qwen2.5-Coder, Deepseek-Coder and CodeLlama variants), and (3) Superior instruction-following capabilities (41.5%–118% improvements) with controlled code generation degradation (–8.9% to –35% across architectures).

The proliferation of spatial data applications (e.g., location-based services) has driven massive outsourcing of geospatial datasets to public clouds, yet exposing critical vulnerabilities to external hackers and internal adversaries. To address the dual challenges of securing fundamental geometric range queries while supporting dynamic updates, this paper proposes a dynamic searchable symmetric encryption scheme for geometric range search that complies with both forward privacy and backward privacy. Specifically, we build a multi-level balanced matching query index and constructthree layers of ciphertext to perform screeningand verification, enhancing query efficiencythrough three rounds of matching andscreening. Furthermore, we establish dual index directories deployed across two isolated servers, implementing cross-server query verification to guarantee single-dimensional range visibility per node. This architecture effectively conceals access patterns through spatial information segregation. Subsequently, we propose a ciphertext-update mechanism with embedded covert cryptographic operators, systematically detecting and neutralizing information leakage during update operations, thereby preserving both forward and backward privacy. Conclusively, comprehensive empirical evaluations demonstrate the scheme's operational efficiency and real-world applicability.

Driven by legal and service demands, redactable blockchains (RBC) have been proposed to balance the editability and immutability of blockchain technology. However, RBC may allow the same block to have multiple valid versions, which malicious nodes could exploit to deceive lightweight nodes, thereby compromising the consistency and integrity of the ledger. To address this issue, we propose an efficient auditing and querying scheme for verifiable redactable blockchain (EAQ-VRBC). It is an auditable verifiable data streaming (VDS) protocol that establishes the RSA accumulator-based revocation mechanism to invalidate outdated blocks in RBC. The trusted node maintains a revocation list stored in a dynamic RSA accumulator. The validity of new blocks is verified by non-membership proofs, ensuring that only the latest data version is considered valid, effectively preventing old data version deception. Compared with existing auditable VDS protocols tailored for RBC, the verification cost of EAQ-VRBC in the query and audit phases is reduced by approximately one order of magnitude. Additionally, EAQ-VRBC uses identity-based RSA signature auditing to ensure data integrity. Finally, security analysis and performance evaluation confirm the feasibility and efficiency of the proposed solution.

With the proliferation of mobile devices and the advancement of cloud computing capabilities, cloud-assisted Internet of Things (IoT) attracts increased attention based on its computational and storage advantages. Upon these conveniences, there also raises privacy concerns that numerous solutions have been proposed to solve it. However, existing methods suffer from challenges such as low retrieval accuracy, inefficiency in large-scale image retrieval, and lack of efficient result verification. In this article, we propose an efficient verifiable privacy-preserving image retrieval scheme (EVPIR). Specifically, we design a hierarchical graph index to significantly enhance retrieval efficiency, which organizes image feature vectors into a multilevel structure, establishing connections between neighboring nodes within each layer and creating a highly structured and efficient retrieval framework. During the retrieval process, we employ a greedy search algorithm to navigate these connections and identify the closest neighbors across different levels, which makes the proposed multilevel approach reduce the search space at each level, achieving faster and more accurate retrieval. Furthermore, we design an efficient dynamic verifiable framework leveraging Chameleon hash functions and BLS signatures where we utilize Chameleon hash nodes based on Merkle hash trees (MHTs) to enable dynamic updates of the verification tree and employ BLS signatures to construct multiple verification nodes for effectively shortening the verification path. Finally, security analysis shows that EVPIR can defend various threat models and extensive experiments further demonstrate that EVPIR can improve retrieval and verification efficiency.

As a pivotal link between visual and linguistic relationships, image-text cross-modal retrieval has received widespread attention. However, existing studies primarily focus on intricate machine learning models to enhance retrieval accuracy and overlook the critical aspect of privacy preservation for images and texts, rendering them unsuitable for lightweight IoT environments. To tackle these challenges, we propose LPCR-IoT, a lightweight and privacy-preserving cross-modal retrieval scheme tailored to IoT environments. LPCR-IoT employs knowledge distillation to train lightweight student models for extracting feature vectors from images and texts, subsequently embedding them into a unified semantic space. Significantly, we propose a new training metric (i.e., Intra-modal Consistent Contrast Loss), which improves the retrieval accuracy by increasing the semantic consistency of the image and text in the common embedding space. Additionally, a novel quadtree index structure leveraging hybrid representation vectors is designed to effectively mitigate retrieval overhead, where feature vectors of images and texts alongside representation vectors are encrypted using a secure kNN algorithm based on LWE, enabling image-text matching in a large-scale ciphertext environment. Finally, we provide a detailed formal analysis to evaluate the security of LPCR-IoT and validate its practicality through extensive experiments on three real-world datasets, namely COCO, Flickr30k and NUS-WIDE.

In recent years, extensive research has been conducted on the design of electricity auctions. However, most existing solutions focus mainly on privacy preservation, efficiency, and bidding strategies, while overlooking the critical issue of key parameters distribution—particularly in day-ahead electricity auctions. At the start of an auction, it is essential to distribute key parameters to all participants in a timely, synchronized, and efficient manner. To address the challenges of fairness, collusion resistance, and efficiency in the pre-distribution of key parameters, we propose an efficient and collusion-resistant key parameters pre-distribution system for day-ahead electricity auctions. As a core component, we design a novel cryptographic primitive, termed identity-based timed-release proxy re-encryption. Based on the truncated q-ABDHE hardness assumption, we instantiate two concrete schemes tailored to different security requirements. The proposed schemes eliminate the need for public key authenticity verification, support efficient privilege revocation, and provide resistance against proxy-bidder collusion attacks. Additionally, the schemes feature user-scale-independent key and ciphertext sizes, leading to significantly improved system efficiency compared to existing approaches.

Encrypted deduplication is attractive because it can provide high storage efficiency while protecting data privacy. Most existing schemes achieve encrypted deduplication against brute-force attacks (BFAs) based on server-aided encryption. Unfortunately, the centralized key server in server-aided encryption can potentially become a single point of failure. To this end, distributed server-aided encryption is presented, which splits a system-level master key into multiple shares and distributes them across several key servers. However, it is hard to improve security and scalability with this method simultaneously.This paper presents a secure and scalable encrypted deduplication scheme ScalaDep. ScalaDep achieves a new design paradigm centered on location-hiding secret sharing of data keys. As the number of deployed key servers increases, the attack cost of adversaries increases while the number of requests handled by each key server decreases, enhancing both scalability and security. Furthermore, we propose a two-phase duplicate detection method for our paradigm, which utilizes short hashes and key identifiers to achieve secure duplicate detection against BFAs. Additionally, based on the allreduce algorithm, ScalaDep enables all key servers to collaboratively record the number of client requests and resist online BFAs by enforcing rate limiting. Security analysis and performance evaluation demonstrate the security and efficiency of ScalaDep.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial data query services have attracted extensive attention. To protect the confidentiality of sensitive information outsourced to cloud servers, much efforts have been devoted to designing geometric range query schemes over encrypted spatial data without affecting availability. However, existing works focus on the privacy-preserving schemes with traditional tree indexes, causing more computing and storage issues. In this paper, we propose an efficient conjunctive geometric range query scheme over encrypted spatial data with a learned index. In particular, we design a new privacy-preserving learned index for spatial data to reduce the search space and storage overhead. The main idea is to add noise disturbance to the objective function instead of directly adding it to output results, reducing the leakage of private information and ensuring the correctness of output results. Moreover, we propose a spatial segmentation algorithm to avoid accessing a large number of unnecessary Z codes in the query process. The formal security analysis shows that our scheme ensures index data security and query privacy. Simulation results show that the query efficiency is improved while the storage overhead is significantly reduced compared with the state-of-the-art schemes.

With growing numbers of users outsourcing images to cloud servers, privacy-preserving content-based image retrieval (CBIR) is widely studied. However, existing privacy-preserving CBIR schemes have limitations in terms of low search accuracy and efficiency due to the use of unreasonable index structures or retrieval methods. Meanwhile, existing result verification schemes do not consider the privacy of verification information. To address these problems, we propose a new secure verification encrypted image retrieval scheme. Specifically, we design an additional homomorphic bitmap index structure by using a pre-trained CNN model with modified fully connected layers to extract image feature vectors and organize them into a bitmap. It makes the extracted features more representative and robust compared to manually designed features, and only performs vector addition during the search process, improving search efficiency and accuracy. Moreover, we design a reversible data hiding (RDH) technique with color images, which embeds the verification information into the least significant bits of the encrypted image pixels to improve the security of the verification information. Finally, we analyze the security of our scheme against chosen-plaintext attacks (CPA) in the security analysis and demonstrate the effectiveness of our scheme on two real-world datasets (i.e., COCO and Flickr-25 k) through experiments.

HPC-cloud is becoming popular as it allows supercomputers to provide computing service with parallelism and high performance based on public cloud technology. Moreover, supercomputers across organizations are forming a network to scale the computational and storage capability of their service. However, the distributed computing process in the supercomputer internet requires a large amount of data transmission and access, arousing privacy and control problems. In this paper, we propose HPCBL, a blockchain-enabled decentralized data computing architecture that ensures private data sharing and computing in the Supercomputer Internet. We also propose a decentralized authentication scheme that entitles users the full control of their anonymous identity to support user private interactions with the Supercomputing Internet. The scheme supports anonymous self-derivative credentials for pair-wised access control and user-optional accountability without a trusted arbiter. We implemented a HPCBL prototype to empirically assess its performance.

Encrypted deduplication is attractive for outsourced storage as it provides both data confidentiality and storage savings. Conventional encrypted deduplication schemes protect data confidentiality based on expensive cryptographic primitives, leading to performance degradation. Recently, several SGX-based schemes have been proposed to accelerate encrypted deduplication. However, these schemes have limitations in both security and performance aspects. This paper presents a SGX-based basic scheme to address these limitations, which first performs proof of ownership (PoW), followed by key generation and data encryption, realizing a new paradigm known as PoW-before-encryption (PbE) to solve the security issue in existing schemes. Additionally, the basic scheme implements deduplication-before-encryption (DbE) to reduce redundant computations, thus improving performance. Despite these improvements, the duplicate detection and key generation in the basic scheme still involve redundant computations. Consequently, we propose an epoch-based enhanced scheme that utilizes data locality and computation deduplication, which caches fresh computations in an epoch and reuses them to enhance performance. We provide a security analysis and evaluate the performance of our schemes using both synthetic and real-world workloads. The results demonstrate that our schemes offer stronger security guarantees while outperforming state-of-the-art schemes in terms of performance.

Passwords are today’s dominant form of authentication, and password guessing is the most effective method for evaluating password strength. Most password guessing models (e.g., PCFG, Markov, and RFGuess) regard passwords as sequences composed of basic units (i.e., characters/segments), with the information flow being unidirectional (i.e., predicting the next unit based on the preceding units). However, modeling passwords in a single direction fails to capture users’ password creation behavior that is actually impacted by the full password context. Through an in-depth analysis of real-world passwords, we reveal that users often create passwords around a central keyword, like common words, names, or dates, and then embellish them with numbers or symbols. Based on this observation, we, for the first time, attempt to parse passwords as trees. Unlike existing sequence models, trees can reveal the semantic connections within passwords and the logical thought processes users follow when creating passwords. For instance, in the passwordiloveyou, the basic unitsiandyouare semantically dependent on the predicatelove, forming a natural tree structure.We propose a trawling guessing model called PassTree and a targeted guessing model based on personally identifiable information (PII), named PassTree-PII. Our extensive experiments demonstrate the effectiveness of our models: (1) PassTree outperforms its leading counterparts by 0.38%-2.51% when guessing numbers are below$10^{7}$107; (2) PassTree-PII achieves a cracking rate comparable to the state-of-the-art RFGuess-PII proposed in USENIX Security’23, but operates significantly more efficiently, using only 0.87% of the memory and being 16.60 times faster. Our work provides a new perspective on understanding user passwords and demonstrates a feasible technical route of applying tree structures to password guessing.

The booming 5G cellular networks sparked tremendous interest in supporting more sophisticated critical use cases through vehicle-to-network (V2N) communications. However, the inherent technical vulnerabilities and densification of 5G raise new security and efficiency challenges. The existing secondary authentication fails to support multi-service access. The random access process lacks authentication of the gNB, possibly leading to fake base station attacks (FBS). Moreover, related research extends key forward/backward secrecy (KF/BS) to require that it also applies to gNBs, thus invalidating most existing schemes. This paper introduces a comprehensive security framework for 5G-V2N that seamlessly integrates with existing standardized architecture to provide privacy-preserving mutual authentication and key agreement for the full service cycle. Specifically, we propose new secondary authentication involving gNBs and support single request access to multi-services. Second, incorporating the service migration idea, we design the g2g (gNB-to-gNB) channel establishment phase to promote secure context share. Finally, the proposed efficient handover phase achieves the security properties of enhanced KF/BS, known randomness secrecy and privacy-preserving, and avoids FBS. We verify the proposed protocol using three different formal techniques: provably secure, BAN-logic, and AVISPA tool. Extensive experimental results and comparison show that our scheme excels in computational and communication efficiencies, and detecting malicious events.

Side-channel attacks are widespread in client-side deduplication systems, compromising the privacy of outsourced data. The adversary may infer the existence status of data via the deterministic relations between duplication requests and responses to launch side-channel attacks. Random Response (RARE) is one of the state-of-the-art approaches to overcome this issue, where the cloud server returns the randomized deduplication response for two requests at once to mitigate the risk of side-channel attacks. However, it still has some inherent limitations on communication efficiency and security. In this paper, we propose Random Coding Responses (RACORE), a lightweight and secure multi-chunk coding algorithm to address the limitations of RARE. RACORE achieves efficient multi-chunk coding and obfuscation based on the linear mapping induced by a specially constructed pseudo-random matrix. Compared to existing schemes, RACORE can strike a flexible balance between security and performance by adjusting parameters. Further, we present an enhanced composite matrix generation strategy to extend the coding matrix. Based on this strategy, we design an enhanced coding algorithm RACORE$^+$to improve efficiency. Besides, we put forward a novel redundant chunk selection method to enhance the security of RACORE and RACORE$^+$. Rigorous security analysis and extensive experimental evaluation demonstrate that both RACORE and RACORE$^+$can effectively resist side-channel attacks while reducing overhead compared to existing schemes.

Popularity-based secure deduplication scheme classifies data based on their number of owners and provides different levels of security for a trade-off between privacy preservation and storage savings. Most existing schemes rely on a trusted third party to record data popularity using deterministic tags, which is impractical in reality. Recently, Ha et al. propose a scheme that uses random tags to record popularity without the need for a trusted third party. However, their scheme is vulnerable to a malicious cloud launching smuggle attacks (SAs) and popularity-faking attacks (PFAs), which poses security vulnerabilities. In this paper, we propose a privacy-preserving popularity-based deduplication scheme. For one thing, we use unforgeable random tags to record data popularity, which defends against SAs. For another thing, we design a verifiable interactive popularity detection scheme to assure the correctness of popularity detection and resist PFAs. Security analysis and evaluation results show that our proposed scheme provides stronger security guarantees with limited overhead compared with existing schemes.

Client-side deduplication system is widely used in cloud storage systems to reduce storage and communication overhead by eliminating the storing and uploading of duplicate data. However, it is vulnerable to side channel attacks, where an adversary can infer the existence status of uploaded data based on deterministic relations in duplication check responses. To address this issue, Random Response (RARE) was proposed, which sends duplication check requests for two chunks simultaneously. Nevertheless, RARE has limitations in terms of communication efficiency and security. In this paper, we propose Random Zero-one Coding Response (RZCR) to overcome these limitations. RZCR achieves lightweight coding of multiple chunks using a novel linear mapping algorithm, allowing for a balance between security and performance through the adjustment of system parameters. Furthermore, we, for the first time, formally define a security game to model these side channel attacks in client-side deduplication systems and introduce a stronger threat model compared to RARE. Security analysis demonstrates that RZCR effectively mitigates the risk of side channel attacks. We also implement a prototype of RZCR and evaluate its performance using large-scale real-world datasets (i.e., Enron Email and Fslhomes) as well as synthetic datasets. The results show that RZCR significantly reduces communication overhead compared to representative schemes.

With the rapid development of cloud computing, more and more users are storing sensitive data on cloud servers, making the privacy-preserving of data particularly important. Dynamic searchable symmetric encryption enables efficient retrieval of encrypted data in cloud computing environments while preserving data privacy. However, existing solutions are not effective in defending against various query-recovery attacks. Therefore, this paper focuses on the privacy-preserving of dynamic searchable symmetric encryption, and proposes a privacy-preserving dynamic searchable symmetric encryption based on anonymization and differential privacy – DADP. Firstly, the original indexes are synthesized into fake indexes using the anonymization hash technology. The synthetic indexes possess randomness and irreversibility, making it impossible for adversaries to infer the generation process of the synthetic indexes or recover the original indexes. Additionally, by using differential privacy to process composite indexes, the privacy of keywords and index information is protected, preventing adversaries from inferring sensitive information based on query results. This approach provides dual privacy-preserving. Compared to other schemes, our scheme achieves type-I backward privacy and can withstand seven types of query recovery attacks. And it improves update and query efficiency by 10-100 times.

The retrieval of encrypted images in cloud computing is a research hotspot at present. However, the existing schemes have the problem of low image retrieval accuracy since it is difficult to obtain accurate feature information through convolutional neural network from encrypted image. In this paper, a secure image retrieval method using dual-stream convolution structure for feature fusion is proposed. Specifically, the stream cryptographic image containing contour features and the Fourier transform image containing frequency domain features are used as the two inputs of the convolutional network stream, where the weighted average gate function helps to integrate the feature information in these streams to achieve a more comprehensive feature representation and improve the retrieval accuracy. Furthermore, to keep the contour features of the encrypted image, the fuzzy image and Arnold mapping algorithm are selected randomly to encrypt the encrypted image twice. Experimental evaluation on three datasets, MNIST, Fashion-MNIST and CIFAR-100, shows that the proposed encryption method has higher security than AES and stream cipher encryption. In addition, the retrieval average accuracy (mAP) of this scheme is superior to the existing methods.

With growing numbers of users outsourcing images to cloud servers, privacy-preserving content-based image retrieval (CBIR) is widely studied. However, existing privacy-preserving CBIR schemes have limitations in terms of low search accuracy and efficiency due to the use of unreasonable indexing structures or retrieval methods. Meanwhile, existing result verification schemes do not consider the privacy of verification information. To address these problems, we propose a new secure verification encrypted image retrieval scheme. Specifically, we design an additional homomorphic bitmap index structure by using a pre-trained CNN model with modified fully connected layers to extract image feature vectors and organize them into a bitmap. It makes the extracted features more representative and robust compared to manually designed features, and only performs vector addition during the search process, improving search efficiency and accuracy. Moreover, we design a reversible data hiding (RDH) technique with color images, which embeds the verification information into the least significant bits of the encrypted image pixels to improve the security of the verification information and reduce the storage overhead. Finally, we analyze the security of our scheme against chosen-plaintext attacks (CPA) in the security analysis and demonstrate the effectiveness of our scheme on two real-world datasets (i.e., COCO and Flickr-25k) through experiments.

Honey encryption (HE) protected password vaults (called honey vaults) are promising tools that allow a user to store multiple passwords (called a password vault) and encrypt them with a master password using HE. In case password vaults are somehow leaked and the attackers launch offline password guessing, honey vaults can yield decoy password vaults for incorrect guesses, forcing an offline guessing attacker to interact with the authentication server to identify whether passwords in decrypted vaults are correct or not. Therefore, honey vaults transform the offline guessing attacker into an online guessing attacker, i.e., honey vault distinguishing attacker.In online guessing, attackers can adopt various attacks to perform multiple guesses against multiple vaults, but the existing theoretical message recovery (MR) security for HE only focuses on the advantage of one-time guess against a single vault, which cannot accurately model realistic attackers and thus can not provide practical advice for users’ vault security. To address this issue, we propose a theoretically-grounded optimal strategy for distinguishing attackers, and manage to derive a much tighter upper bound on the advantage against MR security. Particularly, we provide much tighter upper/lower bounds for advantage against HE-related cryptographic security games, i.e., the security of distribution transforming encoder (DTE), known message attack, and known side information attack. This provides a better understanding of the actual security of honey encryption.To better understand the security of honey vault systems, we instantiate our optimal strategy into three practical attacks and propose an encoding attack. Extensive experiments against two major honey vault systems demonstrate that our four attacks can improve the attack success rate by 1.15-4.35 times compared with their counterparts. For the intersection attack, we propose a feature attack against Cheng et al.’s incremental update mechanism (at USENIX SEC’21), and our attack can breach their mechanism with 87%-93% advantage.

Individuals and companies increasingly adopt encrypted deduplication systems for their enhanced security and efficiency benefits. Server-aided encrypted deduplication systems are the state-of-the-art scheme to resist brute-force attacks. However, it is overly reliant on a single centralized key server and vulnerable to a single point of failure. To this end, existing schemes have implemented distributed key servers based on secret sharing of the master key to resist a single point of failure. Nevertheless, this design has some inherent limitations in balancing security and scalability. Secret sharing of the master key effectively mitigates single points of failure, while negatively impacting system scalability. To address the above limitations, we propose a scalable client-side encrypted deduplication with distributed key servers based on secret sharing of the data key. To resist brute-force attacks, we also design a double-layer matching mechanism to achieve secure and effective duplicate check and key delivery. Additionally, drawing inspiration from random oracle models, we put forward a pseudo-random response strategy for key servers to safeguard key privacy effectively. Rigorous theoretical analysis and extensive experiments demonstrate that our scheme achieves both security and scalability, which is well-suited for deployment in large-scale systems and offers robust protection against a single point of failure.

It is non-trivial to provide semantic security for user data while achieving deduplication in cloud storage. Some studies deploy a trusted party to store deterministic tags for recording data popularity, then provide different levels of security for data according to popularity. However, deterministic tags are vulnerable to offline brute-force attacks. In this paper, we first propose a popularity-based secure deduplication scheme with fully random tags, which avoids the storage of deterministic tags. Our scheme uses homomorphic encryption (HE) to generate comparable random tags to record data popularity and then uses the binary search in the AVL tree to accelerate the tag comparisons. Besides, we find the popularity tamper attacks in existing schemes and design a proof of ownership (PoW) protocol against it. To achieve scalability and updatability, we introduce the multi-key homomorphic proxy re-encryption (MKH-PRE) to design a multi-tenant scheme. Users in different tenants generate tags using different key pairs, and the cross-tenant tags can be compared for equality. Meanwhile, our multi-tenant scheme supports efficient key updates. We give comprehensive security analysis and conduct performance evaluations based on both synthetic and real-world datasets. The results show that our schemes achieve efficient data encryption and key update, and have high storage efficiency.

E-Voting is widely used in many social, economic, political and cultural fields for its convenience, efficiency and greenness, but how to guarantee the fairness of e-voting and the controllability of human intervention needs further in-depth research and exploration. Although the introduction of homomorphic encryption algorithm solves the problem of ballot privacy calculation, and most of these schemes solve the problem of private key confidentiality by using or overlaying multiple different methods of saving private keys, its security will be questioned as long as there is a possibility of human intervention in the saving process. To solve this problem, we propose a timed-release e-voting scheme based on Paillier homomorphic encryption. We analyze the semantic security of the ballot formally by defining the security game, and realize the legitimacy check of the ballot ciphertext through the idea of partial knowledge proof. Property analysis shows that this scheme satisfies the basic properties of the security requirements of the e-voting scheme. Performance analysis shows that this scheme is feasible to implement in practical voting.

Binary packing, a widely-used program obfuscation style, compresses or encrypts the original program and then recovers it at runtime. Packed malware samples are pervasive---they conceal arresting code features as unintelligible data to evade detection. To rapidly respond to large-scale packed malware, security analysts search specific binary patterns to identify corresponding packers. The quality of such packer patterns or signatures is vital to malware dissection. However, existing packer signature rules severely rely on human analysts' experience. In addition to expensive manual efforts, these human-written rules (e.g., YARA) also suffer from high false positives: as they are designed to search the pattern of bytes rather than instructions, they are very likely to mismatch with unexpected instructions.

The Internet of Things (IoT) connects everyday devices and generates real-time data that have greatly prompted business and life efficiency. The integration of IoT and blockchain has made IoT data management and storage more trustworthy. However, despite the immutability property contributes a lot to the trustable reputation of blockchain-based IoT systems, from a data processing perspective, it is desired to achieve skillful and secure blockchain rewriting for scenarios such as device data sharing. Existing blockchain rewriting solutions usually rely on centralized modifiers where the rewriting power is difficult to control or withdraw. In this article, we propose a new auditable redactable blockchain (RB) scheme named ACHR that supports self-management and mandatory revocation of the rewriting privilege. The scheme allows user devices to rewrite their blockchain transactions under strict auditing to ensure content security. To prevent centralization or rewriting power abuses, the revocation trapdoor can be computed compulsorily by an auditor when a redaction is published to the blockchain. We introduce a generic construction and an instantiation of the ACHR scheme for building the RB and prove its security. We provide a prototype implementation to demonstrate that our scheme is effective and efficient compared to the traditional blockchain-IoT system with immutability.

The server-aided encryption is widely used in encrypted deduplication systems to protect against brute-force attacks. However, it is non-trivial to update the master key managed by the key server in existing schemes. Once the master key is leaked, all user data are vulnerable to offline brute-force attacks. In this article, we extend the server-aided encryption with the updatable encryption (UE) and a dynamic proof of ownership (PoW) protocol to make it support efficient key updates and can be used in the client-side deduplication. Specifically, we design an updatable server-aided encryption scheme based on UE, which achieves efficient encryption and the user-transparent key update for a system-level master key. Besides, to further enable our updatable server-aided encryption to be applicable to the client-side deduplication, we propose a dynamic PoW protocol based on the Merkle tree. Compared to the state-of-the-art PoW scheme, our PoW protects data privacy and allows multi-time leakages for the target file. Finally, we analyze the security of our scheme and present the performance evaluation. The results show that our scheme provides comprehensive security protection for user data and achieves efficient encryption, PoW, and key update.

Dynamic searchable encryption, which allows clients to outsource their encrypted data to cloud servers and retain the ability to query and update data, has received wide attention. In the setting, it is essential to ensure that a server infers as little as possible about the content of the outsourced data and the queries it processes. In this article, we propose a forward and backward secure searchable encryption scheme on bipartite graphs (FBSSE-BG) that offers the strongest level of backward security. In particular, we introduce the notion of update counter to construct a new bi-directional index structure, which realizes conjunctive queries over bipartite graphs and supports flexible updates of outsourced data. Besides, to minimize the information revealed to servers, we propose a new oblivious data structure to store the bi-directional index and use a semantically-secure encryption scheme to encrypt node information, such that servers can only observe a series of ORAM locations and encrypted paths. Finally, we prove the security of FBSSE-BG by using the real-world versus ideal-world formalization and provide experimental efficiency evaluations for its implementations.

With the development of various applications of blockchain, blockchain-assisted searchable encryption technology has received wide attention as it can eliminate misbehaviours of malicious servers through the verification and incentive mechanism of blockchain. However, most existing solutions update the encrypted data by means of appending new transactions, which does not scale and wastes resources. In this paper, we explore the potential of redactable blockchain and propose a privacy-preserving dynamic searchable encryption framework (DSE-RB), which is a general scheme that guarantees reliable queries and updates on encrypted data. In particular, we first use transaction-level editing technology to achieve a more flexible update operation of encrypted data without additional transactions while avoiding the waste of storage on the chain. To better support practical applications, we use an index partition method to divide the traditional binary tree index into a plurality of sub-indexes and introduce the concept of polynomials to simplify the whole access control mechanism. We define the security model and conduct repeated experiments on real data sets to test the efficiency. Experimental results and theoretical analysis show the practicability and security of our scheme.

Message-locked Encryption (MLE) is the most common approach used in encrypted deduplication systems. However, the systems based on MLE are vulnerable to frequency analysis attacks, because MLE encrypts the identical plain texts into the identical ciphertexts, which is deterministic. The state-of-the- art defense scheme, which named TED, lacks key verification and uses a single key server to record frequency information. Once the key server is compromised, TED will be vulnerable to brute-force attacks. In addition, TED's key generation algorithm needs to be designed more exquisitely to strengthen protection, and its security indicator is not comprehensive. We propose SDAF, which supports key verification and enhanced protection against frequency analysis attacks. Based on chameleon hash, SDAF realizes key verification to prevent malicious key servers from generating fake encryption keys. In order to disturb the frequency information, SDAF introduces reservoir sample to generate uniformly distributed encryption keys, and uses multiple key servers, which interact with each other via multi-party PSI and rotate spontaneously to avoid the single point of failure. Moreover, a new indicator Kurtosis is pointed out to evaluate the security against frequency analysis attacks. We implement the prototypes of SDAF. The experiments of the real-world data sets show that, compared with the existing schemes, SDAF can better resist frequency analysis attacks with lower time overheads.

As the most dominant authentication mechanism, password-based authentication suffers catastrophic offline password guessing attacks once the authentication server is compromised and the password database is leaked. Password hardening (PH) service, an external/third-party crypto service, has been recently proposed to strengthen password storage and reduce the damage of authentication server compromise. However, all existing schemes are unreliable in that they overlook the important restorable property: PH service opt-out. In existing PH schemes, once the authentication server has subscribed to a PH service, it must adopt this service forever, even if it wants to stop the external/third-party PH service and restore its original password storage (or subscribe to another PH service). To fill the gap, we propose a new PH service called PW-Hero that equips its PH service with an option to terminate its use (i.e., opt-out). In PW-Hero, password authentication is strengthened against offline attacks by adding external secret spices to password records. With the opt-out property, authentication servers can proactively request to end the PH service after successful authentications. Then password records can be securely migrated to their traditional salted hash state, ready for subscription to other PH services. Besides, PW-Hero achieves all existing desirable properties, such as comprehensive verifiability, rate limits against online attacks, and user privacy. We define PW-Hero as a suite of protocols that meet desirable properties and build a simple, secure, and efficient instance. Moreover, we develop a prototype implementation and evaluate its performance, establishing the practicality of our PW-Hero service.

App repackaging has been raising serious concerns about the health of the Android ecosystem, and repackage-proofing is an important mitigation against threat of such attacks. However, existing app repackage-proofing schemes were only evaluated against trivial adversaries simulated using analyzers for other purposes (e.g., disclosing privacy leakage vulnerabilities), hence were shown “effective” mainly because their key programming features were not even supported by those toolkits. Furthermore, existing works have also neglected dynamic adversaries capable of manipulating victim apps at runtime, making them vulnerable against such stronger opponents. In this article, we propose a novel repackage-proofing framework, which deploys distributed detection and response sites into the subject app's native partition to cross-verify all its code files. The detection sites transmit obtained integrity metrics to response sites via secure communication channels built on the subject app's own control flows using a specialized obfuscation technique based on Collatz conjecture, turning the repackage-proofing process into complicated implicit flows that are intrinsically difficult to be resolved due to the conjecture's nonlinear dynamical behaviors. We evaluated our framework using sophisticated Android data-flow analyzers. Results showed that our prototype effectively impeded analyses aiming to trace the information flows of its cross-verification.

App repackaging has raised serious concerns to the Android ecosystem with the repackage-proofing technology attracting attention in the Android research community. In this article, we first show that existing repackage-proofing schemes rely on a flawed security assumption, and then propose a new class ofactive warden attackthat intercepts and falsifies the metrics used by repackage-proofing for detecting the integrity violations during repackaging. We develop a proof-of-concept toolkit to demonstrate that all the existing repackage-proofing schemes can be bypassed by our attack toolkit. On the positive side, our analysis further identifies a new integrity metric in the Android ART runtime that can robustly and efficiently indicate bytecode tampering caused by either repackaging or active warden attacks. By associating this new metric with two supplemental verification mechanisms, we construct a multi-party verification framework that significantly raises the bar of repackage-proofing and identify conditions under which the proposed framework could detect app repackaging without getting compromised by active warden attacks.

Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This article evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We demonstrate the practicality and generality of such side-channel exploitation with a variety of assumed attack scenarios. Simulations on both Nvidia and Intel graphics adapters showed that our attack could achieve high accuracy, while in-depth studies were also presented to explore the low-level rationale behind such effectiveness. On top of that, we further propose a practical mitigation scheme which protects GPU workloads against the graphics-interrupt-based side-channel attack by piggybacking mask payloads on them to generate interfering graphics interrupt “noises”. Experiments show that our mitigation technique effectively prohibited spy processes from inferring user behaviors via analyzing runtime patterns of graphics interrupt with only trivial overhead.

It is difficult to provide semantic security for user data while using deduplication to save storage space in cloud storage. Some studies attempt to provide different levels of security for data according to their popularity for a reasonable trade-off between security and efficiency. However, existing schemes generally need a trusted third party to store deterministic data tags to record data popularity. If the trusted third party is compromised by adversaries, the deterministic tags will expose data information. In this paper, we propose a popularity-based secure deduplication scheme with fully random tags, which does not need to store deterministic tags. Our solution is using the homomorphic encryption to generate comparable random tags to record data popularity and using binary search to reduce time complexity of tag comparison to logarithmic time. Besides, we also design a proof of ownership protocol based on homomorphic encryption to prevent adversaries with only the data tag from tampering with the data popularity, which are not considered in the existing popularity-based schemes. We implement our scheme for system efficiency evaluation. Compared with the scheme of Stanek et al., our scheme has a slight improvement in encryption efficiency.

Cryptographic primitive of timed-release encryption (TRE) enables the sender to encrypt a message which only allows the designated receiver to decrypt after a designated time. Combined with other encryption technologies, TRE technology is applied to a variety of scenarios, including regularly posting on the social network and online sealed bidding. Nowadays, in order to control the decryption time while maintaining anonymity of user identities, most TRE solutions adopt a noninteractive time server mode to periodically broadcast time trapdoors, but because these time trapdoors are generated with fixed time server’s private key, many “ciphertexts” related to the time server’s private key that can be cryptanalyzed are generated, which poses a big challenge to the confidentiality of the time server’s private key. To work this out, we propose a concrete scheme and a generic scheme of security-enhanced TRE (SETRE) in the random oracle model. In our SETRE schemes, we use fixed and variable random numbers together as the time server’s private key to generate the time trapdoors. We formalize the definition of SETRE and give a provably secure concrete construction of SETRE. According to our experiment, the concrete scheme we proposed reduces the computational cost by about 10.8% compared to the most efficient solution in the random oracle model but only increases the almost negligible storage space. Meanwhile, it realizes one-time pad for the time trapdoor. To a large extent, this increases the security of the time server’s private key. Therefore, our work enhances the security and efficiency of the TRE.

Password strength meters (PSMs) are being widely used, but they often give conflicting, inaccurate and misleading feedback, which defeats their purpose. Except for fuzzyPSM, all PSMs assume passwords are newly constructed, which is not true in reality. FuzzyPSM considers password reuse, six major leet transformations and initial capitalization, and performs the best as evaluated by Golla and Dürmuth at ACM CCS’18. On the basis of fuzzyPSM, we propose a new PSM based onReuse,Leet andSeparation, namely RLS-PSM. First, we classify password reuse behaviors into capitalization and those that use special characters for leet or separation, and calculate the corresponding probabilities. Then, to balance efficiency and precision, we use Long Short-Term Memory to calculate the probabilities of alphanumeric strings. Besides, we propose to usebenchmark passwordsto show therelative strengthof a password. Due to the varied impacts of different service types and diversified economic value of websites, we consider parameter settings of RLS-PSM under six different service types. Finally, we use the Monte Carlo method and weighted Spearman coefficient to measure and compare the robustness and accuracy of RLS-PSM, leading PSMs (including Markov-based PSM, PCFG-based PSM, fuzzyPSM, RNN, and Zxcvbn), and password cracking tools (including JtR and Hashcat). We find that the robustness of RLS-PSM is significantly higher than all counterparts whenevaluating attempts> 104(e.g., on average, Fraction of Successfully Evaluated passwords of RLS-PSM is 18.9% higher than fuzzyPSM). The accuracy of RLS-PSM is also better than other mainstream PSMs used for comparison in this paper, except for fuzzyPSM.

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber-physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence of such malware threats, therefore, call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and Internet of Things (IoT) devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android is challenging because of restrictions from the security and privacy model of the platform. In this article, the authors investigate how the latest advances in deep learning could address this security problem with better accuracy. Specifically, a deep learning engine is proposed that detects sensitive app behaviors by classifying patterns of system-wide statistics, such as available storage space and transmitted packet volume, using a customized deep neural network based on existing models called Encoder and ResNet. Meanwhile, to handle resource limitations on typical CPS and IoT devices, sparse learning is adopted to reduce the amount of valid parameters in the trained neural network. Evaluations show that the proposed model outperforms a well-established group of baselines on time series classification in identifying sensitive app behaviors with background noise and the targeted behaviors potentially overlapping.

Fuzzing is an effective method to find software bugs and vulnerabilities. One of the most useful techniques is the coverage-guided fuzzing, whose key element is the tracing code coverage information. Existing coverage-guided fuzzers generally use the the number of basic blocks or edges explored to measure code coverage. Path-coverage can provide more accurate coverage information than basic block and edge coverage. However, the number of paths grows exponentially as the size of a program increases. It is almost impossible to trace all the paths of a real-world application. In this paper, we propose a fuzzing solution named PathAFL, which assists a fuzzer by path identification. It can effectively identify and utilize the important h-path, which is a new path but whose edges have all been touched previously. First, PathAFL only inserts one assembly instruction to AFL's original code to calculate the path hash, and uses a selective instrumentation strategy to reduce the tracing granularity of an execution path. Second, we design a fast filtering algorithm to choose higher weight paths from a large number of h-paths and add them to the seed queue. Third, both the seed selection algorithm and the power schedule are implemented based on the path weight. Finally we implemented PathAFL based on the popular fuzzer AFL and evaluated it on 10 well-fuzzed benchmark programs. In 24 hours, PathAFL explored 38% more paths and 9.3% more edges than AFL. Compared with CollAFL-x, the number is 25% and 5.9% correspondingly. Moreover, PathAFL found the more bugs on the LAVA-M dataset, even four unlisted bugs. The results show that PathAFL outperforms the previous fuzzers in terms of both code coverage and bug discovery. In well-tested programs, PathAFL found 8 new security bugs with 6 CVEs assigned.

Dynamic software watermarking is one of the major countermeasures against software licensing violations. However, conventional dynamic watermarking approaches have exhibited a number of weaknesses including exploitable payload semantics, exploitable embedding/recognition procedures, and weak correlation between payload and subject software. This paper presents a novel dynamic watermarking method, Xmark, which leverages a well-known unsolved mathematical problem referred to as the Collatz conjecture. Our method works by transforming selected conditional constructs (which originally belonged to the software to be watermarked) with a control flow obfuscation technique based on Collatz conjecture. These obfuscation routines are built in a particular way such that they are able to express a watermark in the form of iteratively executed branching activities occurred during computing the aforementioned conjecture. Exploiting the one-to-one correspondence between natural numbers and their orbits computed by the conjecture (also known as the “Hailstone sequences”), Xmark's watermark-related activities are designed to be insignificant without the pre-defined secret input. Meanwhile, being integrated with obfuscation techniques, our method is able to resist attacks based on various reverse engineering techniques on both syntax and semantic levels. Analyses and simulations indicated that Xmark could evade detections via pattern matching and model checking, and meanwhile effectively prohibit dynamic symbolic execution. We have also shown that our method could remain robust even if a watermarked software is compromised via re-obfuscation using approaches like control flow flattening.

Cloud computing is growing exponentially, whereby there are now hundreds of cloud service providers (CSPs) of various sizes. While the cloud consumers may enjoy cheaper data storage and computation offered in this multi-cloud environment, they are also in face of more complicated reliability issues and privacy preservation problems of their outsourced data. Though searchable encryption allows users to encrypt their stored data while preserving some search capabilities, few efforts have sought to consider the reliability of the searchable encrypted data outsourced to the clouds. In this paper, we propose a privacy-preserving STorage and REtrieval (STRE) mechanism that not only ensures security and privacy but also provides reliability guarantees for the outsourced searchable encrypted data. The STRE mechanism enables the cloud users to distribute and search their encrypted data across multiple independent clouds managed by different CSPs, and is robust even when a certain number of CSPs crash. Besides the reliability, STRE also offers the benefit of partially hidden search pattern. We evaluate the STRE mechanism on Amazon EC2 using a real world dataset and the results demonstrate both effectiveness and efficiency of our approach.

Cloud brokers have been recently introduced as an additional computational layer to facilitate cloud selection and service management tasks for cloud consumers. However, existing brokerage schemes on cloud service selection typically assume that brokers are completely trusted, and do not provide any guarantee over the correctness of the service recommendations. It is then possible for a compromised or dishonest broker to easily take advantage of the limited capabilities of the clients and provide incorrect or incomplete responses. To address this problem, we propose an innovative cloud service selection verification (CSSV) scheme and index structures (MMBcloud-tree) to enable cloud clients to detect misbehavior of the cloud brokers during the service selection process. We demonstrate correctness and efficiency of our approaches both theoretically and empirically.

Dynamic software fingerprinting has been an important tool in fighting against software theft and pirating by embedding unique fingerprints into software copies. However, the existing work uses the methods from dynamic software watermarking as direct solutions, in which the secret marks are inside rather independent code modules attached to the software. This results in an intrinsic weakness against targeted collusive attacks, since differences among the software copies correspond directly to the fingerprint-related components. In this paper, we suggest a novel mode of the dynamic fingerprinting called integrated fingerprinting, of which the goal is to ensure all the fingerprinted software copies possess identical behaviors at semantic level. We then provide the first implementation of integrated fingerprinting called Neuroprint on top of a control flow obfuscator that replaces program's conditional structures with neural networks trained to simulate their branching behaviors. Leveraging the rich entropy in the outputs of these neural networks, Neuroprint embeds the software fingerprints, such that a one-time construction of the networks serves both the purposes of obfuscation and fingerprinting. Evaluations show that due to the incomprehensibility of neural networks, it is infeasible to de-obfuscate the software transformed by Neuroprint or attack the fingerprint using even the latest program analysis techniques. Revealing information regarding the hidden fingerprints via collusive attacks on Neuroprint is difficult as well. Finally, Neuroprint also demonstrates negligible runtime overhead.

We propose a novel dynamic software watermarking design based on Return-Oriented Programming (ROP). Our design formats watermarking code into well-crafted data arrangements that look like normal data but could be triggered to execute. Once triggered, the pre-constructed ROP execution will recover the hidden watermark message. The proposed ROP-based watermarking technique is more stealthy and resilient over existing techniques since the watermarking code is allocated dynamically into data region and therefore out of reach of attacks based on code analysis. Evaluations show that our design not only achieves satisfying stealth and resilience, but also causes significantly lower overhead to the watermarked program.

Software watermarking is a tool used to combat software piracy by embedding identifying information into a program. Most existing proposals for software watermarking have the shortcoming that they heavily rely on the stealth of watermark to prevent adversaries removing marks. Besides, the watermark is separate from the original program and can be destroyed via fairly straightforward semantics-preserving code transformations. In order to mitigate these problems, this paper introduces SVM-based watermarking, a non-stealthy approach to software watermarking based on the incomprehensibility of support vector machines(SVMs). The advantage of this technique is that software watermarking is handled as the knowledge embedded into SVMs and is closely associated with the program logic. Thus, it makes watermark more difficult to be destroyed and removed. The results of the experiment further indicate that the proposed method is a lightweight and effective software watermarking scheme.

Cloud computing offers a wide array of storage services. While enjoying the benefits of flexibility, scalability and reliability brought by the cloud storage, cloud users also face the risk of losing control of their own data, in partly because they do not know where their data is actually stored. This raises a number of security and privacy concerns regarding one's sensitive data such as health records. For example, according to Canadian laws, data related to personal identifiable information must be stored within Canada. Nevertheless, in contrast to the urgent demands, privacy requirements regarding to cloud storage locations have not been well investigated in the current cloud computing market, fostering security and privacy concerns among potential adopters. Aiming at addressing this emerging critical issue, we propose a novel secure location-sensitive storage framework, called SecLoc, which offers protection for cloud users' data following the storage location restrictions, with minimum management overhead to existing cloud storage services. We conduct security analysis, complexity analysis and experimental evaluation on the proposed SecLoc system. Our results demonstrate both effectiveness and efficiency of our mechanism.

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud for sharing, which brings forth new challenges for outsourced data security and privacy. Attribute-based encryption (ABE) is a promising cryptographic primitive, which has been widely applied to design fine-grained access control system recently. However, ABE is criticized for its high scheme overhead as the computational cost grows with the complexity of the access formula. This disadvantage becomes more serious for mobile devices with constrained computing resources. Aiming at tackling the challenge above, we present a generic and efficient solution to implement attribute-based access control system by introducing secure outsourcing techniques into ABE. More precisely, two cloud service providers (CSPs), namely key generation-cloud service provider (KG-CSP) and decryption-cloud service provider (D-CSP) are introduced to perform the outsourced key-issuing and decryption on behalf of attribute authority and users respectively. In order to outsource heavy computation to both CSPs without private information leakage, we formalize an underlying primitive called outsourced ABE (OABE) and propose several constructions with outsourced decryption and key-issuing. Finally, extensive experiment demonstrates that with the help of KG-CSP and D-CSP, efficient key-issuing and decryption are achieved in our constructions.

Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. In this paper, aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting. Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

The path constraints are leaked by conditional jump instructions which are the binary form of software's internal logic. Based on the problem of above, reverse engineering using path-sensitive techniques such as symbolic execution and theorem proving poses a new threat to software intellectual property protection. In order to mitigate path information leaking problem, we propose a novel obfuscation technique called "black box" to combat the state-of-art reverse engineering techniques. By handling the branch conditions as knowledge embedded into black boxes, the black boxes can simulate the behaviors of the obfuscated branch logic, while the original branch condition is hidden. We show that based on the incomprehensibility of black boxes, revealing branch conditions hidden by our method is considerably harder due to the high computational cost. The results of the experiment further indicate that besides providing effective protection, our method is also a light-weight branch obfuscation scheme.

SUMMARY With the rapid development of cloud computing, more and more data are being centralized into remote cloud server for sharing, which raises a challenge on how to keep them both private and accessible. Although searchable encryption provides an efficient solution to support keyword‐based search directly on encrypted data, considering its application in file sharing, existing work depends on key sharing among authorized users, which inevitably causes the risks of key exposure and abuse. In this paper, aiming at enabling efficient and secure data sharing in cloud computing, we provide a generic construction for this purpose. The proposed construction is full‐featured: (i) It enables authorized users to perform keyword‐based search directly on encrypted data without sharing the unique secret key; and (ii) it provides two‐layered access control to limit unauthorized user's access to the shared data. On the basis of the proposed generic construction, we utilize the existing techniques on identity‐based broadcast encryption and public key searchable encryption to instantiate a concrete construction. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Cycle‐walking is a method that makes sure ciphertext falls in the acceptable range through encrypting plaintext repeatedly with some underlying cipher. This technology provides a general way to construct cryptographic schemes for various interesting applications, including enhancing existing system security without the change of original structure, encrypting multimedia data with the preservation of scalability, generating credit card numbers for Web transaction, and so on, which have a common feature that ciphertext is required to satisfy certain restrictions in order to allow some operations directly imposed on encrypted data. Nevertheless, as far as we know, there exists little work making rigorous analysis on cycle‐walking, especially its undeterministic efficiency, which may limit the application of schemes constructed by such technology or even lead it to unpracticality. In this paper, aiming at filling some gaps about cycle‐walking and helping cryptographic theory “catch up” with its application, we present the rigorous analysis on cycle‐walking's properties including consistency, security, and efficiency. On consistency, we show that cycle‐walking will necessarily arrive back with finite iteration rounds and its decryption reverses encryption. On security, we show that cycle‐walking would not degrade the security of underlying ciphers. On efficiency, instead of using “nondeterministic” to describe cycle‐walking's performance in previous work, we make precise analysis and provide the answer to “how long is the duration of cycle‐walking's encrypting process.” Copyright © 2012 John Wiley & Sons, Ltd.