Low Earth Orbit (LEO) satellite networks have become the main differentiator in achieving global connectivity, augmenting terrestrial networks through wider coverage, lower latency, and native integration with 5G/6G, the Internet of Things (IoT), and edge services. However, the expansion of LEO constellations introduces substantial security challenges, mainly ensuring robust authentication under dynamic, resource and bandwidth constrained conditions. In many practical architectures, authentication is performed indirectly, with satellites forwarding verification material to ground infrastructure rather than authenticating autonomously. Despite its prevalence, indirect authentication in LEO networks lacks a dedicated, up-to-date survey and a consistent way to compare designs. This paper reviews 69 indirect authentication protocols published between 1996 and 2024 and introduces a role-based taxonomy that distinguishes relay-based schemes from schemes where satellites provide limited assistance prior to ground-based verification. Each protocol is analysed in terms of architecture, cryptographic approach, security properties, validation practices, and efficiency trade-offs. Emerging directions are also synthesised, including blockchain-based designs, quantum security, physical-layer authentication, and Zero Trust-inspired approaches. The survey consolidates fragmented terminology, clarifies design choices and trade-offs, and highlights open research problems toward scalable authentication for future LEO constellations that reflect operational realities.

Next Generation (NextGen) Low Earth Orbit satellite networks are rapidly expanding to support global communication and 6G technology transition. This growth exposes networks to new security challenges due to wide coverage in hostile areas and increased access points in space and on Earth. Traditional static authentication methods prove inadequate in this dynamic environment. We address these challenges by developing a Zero Trust Authentication Protocol for Inter-Satellite Link (ISL) communication. Our protocol implements a novel verification process that leverages orbital signals to authenticate ISLs. This approach ensures secure data access and transmission exclusively among verified satellites, mitigating threats from eavesdropping, signal spoofing, impersonation, and replay attacks. To optimize security and resource efficiency, we integrate Hyperelliptic Curve Cryptography (HECC) into our protocol. We validate our approach through MATLAB and Systems Tool Kit (STK) simulations, complemented by BAN Logic and Scyther analyses. Our findings demonstrate that our protocol enhances the security framework of NextGen LEO networks without compromising their performance or operational capabilities.

With frequent software-induced activations on DRAM rows, bit flips can occur on their physically adjacent rows (i.e., RowHammer). Existing studies leverage FPGA platforms to characterize RowHammer, which have identified key factors that contribute to RowHammer bit flips, e.g., data pattern. As the FPGA-based studies have removed the interference of the OS and the memory controller, their findings on the identified contributing factors do not always work as reported in a real-world computing system, resulting in negative effects on system-level RowHammer attacks and defenses. In this paper, we carry out a system-level empirical study on factors from both the software side and the DRAM side that contribute to RowHammer. We conduct the study on 33 DRAM modules including both DDR4 and DDR3, with 292 DRAM chips from various vendors. Our experimental results from the software side show that some prior findings about existing factors are inconsistent with our observations, thus not applicable to a real-world system. Also, we contribute to identifying one new factor that effectively affects RowHammer bit flips. Our DRAM-side results identify three types of new contributing factors and indicate that DRAM modules are more vulnerable if they achieve better performance and lower power consumption. Particularly, Intel XMP, intended for improving DRAM performance, might be abused for RowHammer attacks.

Quantum computers are believed to be capable of breaking the security of most classical public key cryptosystems. To mitigate future security risks, researchers have been working on a hybrid approach that uses both classical and post-quantum cryptographic techniques, with the aim of keeping the system secure as long as at least one of the cryptosystems remains secure. However, most existing hybrid cryptosystems require protocol revisions to accommodate post-quantum cryptographic algorithms, leading to extensive modifications of existing code-bases and increased complexity in the state machines. In this paper, we explore a novel generic hybrid model that requires only minimal changes to the codebase of a classical cryptosystem while maintaining the simplicity of the state machines. To illustrate the working principle and provide a benchmark for our generic hybrid model, we conduct a case study on the IKEv2 protocol using the strongS wan library. Our benchmark reveals that, in a hybrid configuration with two protocols, our generic model introduces minimal overhead compared to the combined key exchange time of both protocols. Moreover, our model design allows for the initiation of different protocols in parallel, resulting in an acceleration of the key exchange time, particularly in hybrid configurations Involving more than two protocols.

Zero-knowledge range proofs play a critical role in confidential transactions (CT) on blockchain systems. They are used to prove the non-negativity of committed transaction payments without disclosing the exact values. Logarithmicsized range proofs with transparent setups, e.g., Bulletproofs, which aim to prove a committed value lies in the range [0, 2 -1] where is the bit length of the range, have gained growing popularity for communication-critical blockchain systems as they increase scalability by allowing a block to accommodate more transactions. In this paper, we propose SwiftRange, a new type of logarithmic-sized zero-knowledge range argument with a transparent setup in the discrete logarithm setting. Our argument can be a drop-in replacement for range proofs in blockchain-based confidential transactions. Compared with Bulletproofs, our argument has higher computational efficiency and lower round complexity while incurring comparable communication overheads for CT-friendly ranges, where N ∈ {32, 64}. Specifically, a single SwiftRange achieves 1.73× and 1.37× proving efficiency with no more than 1.1× communication costs for both ranges, respectively. More importantly, our argument is doubly efficient in verification efficiency. Furthermore, our argument has a smaller size when N ≤ 16, making it competitive for many other communication-critical applications. Our argument supports the aggregation of multiple single arguments for greater efficiency in communication and verification. Finally, we benchmarked our argument against the state-of-the-art range proofs to demonstrate its practicality.

Bit-decomposition-based zero-knowledge range proofs in the discrete logarithm (DLOG) setting with a transparent setup, e.g., Bulletproof (IEEE S&P 18), Flashproof (ASIACRYPT 22), and SwiftRange (IEEE S&P 24), have garnered widespread popularity across various privacy-enhancing applications. These proofs aim to prove that a committed value falls within the non-negative range [0, 2^N-1] without revealing it, where N represents the bit length of the range. Despite their prevalence, the current implementations still suffer from suboptimal performance. Some exhibit reduced communication costs at the expense of increased computational costs while others experience the opposite. Presently, users are compelled to utilize these proofs in scenarios demanding stringent requirements for both communication and computation efficiency. In this paper, we introduce, FlashSwift, a stronger DLOG-based logarithmic-sized alternative. It stands out for its greater shortness and significantly enhanced computational efficiency compared with the cutting-edge logarithmic-sized ones for the most common ranges where N is no more than 64. It is developed by integrating the techniques from Flashproof and SwiftRange without using a trusted setup. The substantial efficiency gains stem from our dedicated efforts in overcoming the inherent incompatibility barrier between the two techniques. Specifically, when N=64, our proof achieves the same size as Bulletproof and exhibits 1.1 times communication efficiency of SwiftRange. More importantly, compared with the two, it achieves 2.3 times and 1.65 times proving efficiency, and 3.2 times and 1.7 times verification efficiency, respectively. At the time of writing, our proof also creates two new records of the smallest proof sizes, 289 bytes and 417 bytes, for 8-bit and 16-bit ranges among all the bit-decomposition-based ones without requiring trusted setups. Moreover, to the best of our knowledge, it is the first configurable range proof that is adaptable to various scenarios with different specifications, where the configurability allows to trade off communication efficiency for computational efficiency. In addition, we offer a bonus feature: FlashSwift supports the aggregation of multiple single proofs for efficiency improvement. Finally, we provide comprehensive performance benchmarks against the state-of-the-art ones to demonstrate its practicality.

As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs are more advanced in that they have superior stealthier in bypassing mainstream countermeasures that are effective against SABAs. Nonetheless, existing SSBAs suffer from two major limitations. First, they can hardly achieve a good trade-off between ASR (attack success rate) and FPR (false positive rate). Besides, they can be effectively detected by the state-of-the-art (SOTA) countermeasures (e.g., SCAn [40]).

Searchable encryption enables secure searches over encrypted data in the cloud. Among all paradigms, public key encryption with keyword search (PEKS) is particularly desirable by privacy-preserving distributed computing and IoT applications, since it allows multiple parties (i.e., writers) to independently contribute encrypted data. However, a PEKS search usually requires a linear scan over the entire dataset for keyword search, causing unacceptable latency when facing a large amount of data. All existing efforts to speed up multi-writer searchable encryption are based on conventional hardness assumptions for security, which can be broken provided the advent of quantum computers. In this work, we propose a lattice-based multi-writer searchable encryption scheme, which lets writers build indices for their outsourced data to make keyword searches faster. Meanwhile, the security of the proposed solution relies on the learning with errors assumption, which is known to withstand the potential attack from quantum computers.

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to hammer rows can induce bit flips in neighboringvictim rows. Rowhammer attacks have enabled privilege escalation, sandbox escape, cryptographic key disclosures, etc. A key requirement ofallexisting rowhammer attacks is that an attacker must have access to at least part of an exploitable hammer row. We term such rowhammer attacks as Explicit Hammer. Recently, several proposals leverage the spatial proximity between the accessed hammer rows and the location of the victim rows for a defense against rowhammer. These all aim to deny the attacker's permission to access hammer rows near sensitive data, thus defeating explicit hammer-based attacks. In this paper, we question the core assumption underlying these defenses. We present Implicit Hammer, a confused-deputy attack that causes accesses to hammer rows that the attacker is not allowed to access. It is a paradigm shift in rowhammer attacks since it crosses privilege boundary to stealthily rowhammer an inaccessible row by implicit DRAM accesses. Such accesses are achieved by abusing inherent features of modern hardware and/or software. We propose a generic model to rigorously formalize the necessary conditions to initiate implicit hammer and explicit hammer, respectively. Compared to explicit hammer, implicit hammer can defeat the advanced software-only defenses, stealthy in hiding itself and hard to be mitigated. To demonstrate the practicality of implicit hammer, we have created two implicit hammer's instances, called PThammer and SyscallHammer.

Network Function Virtualisation (NFV) advances the adoption of composable software middleboxes. Accordingly, cloud data centres become major NFV vendors for enterprise traffic processing. Due to the privacy concern of traffic redirection to the cloud, secure middlebox systems (e.g., BlindBox) draw much attention; they can process encrypted packets against encrypted rules directly. However, most of the existing systems supporting pattern matching based network functions require the enterprise gateway to tokenise packet payloads via sliding windows. Such tokenisation induces a considerable communication overhead, which can be over 100× to the packet size. To overcome this bottleneck, in this article, we propose the first bandwidth-efficient encrypted pattern matching protocol for secure middleboxes. We resort to a primitive called symmetric hidden vector encryption (SHVE), and propose a variant of it, aka SHVE+, to achieve constant and moderate communication cost. To speed up, we devise encrypted filters to reduce the number of accesses to SHVE+ during matching highly. We formalise the security of our proposed protocol and conduct comprehensive evaluations over real-world rulesets and traffic dumps. The results show that our design can inspect a packet over 20 k rules within 100$\mu$s. Compared to prior work, it brings a saving of 94 percent in bandwidth consumption.

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. To address these issues, in this article, we introduce a framework of the self-tallying voting system in decentralized IoT based on blockchain. We propose a concrete construction and prove that the proposed system satisfies all the security requirements, including fairness, dispute-freeness, and maximal ballot secrecy. We simulate the algorithms on a laptop, an Android phone, and a Raspberry Pi to test the time consumption and evaluate the gas cost of each algorithm in a private blockchain as well. The implementation results demonstrate the practicability of our system.

Equivocation is one of the most fundamental problems that need to be solved when designing distributed protocols. Traditional methods to defeat equivocation rely on trusted hardware or particular assumptions, which may hinder their adoption in practice. The advent of blockchain and decentralized cryptocurrencies provides an auspicious breakthrough paradigm to resolve the problem above. In this paper, we propose a blockchain-based solution to address contractual equivocation, which supports user-defined fine-grained policy-based equivocation. Specifically, users will be de-incentive if the statements they made breach the predefined access rules. The core of our solution is a newly introduced primitive named Policy-Authentication-Preventing Signature (PoAPS), which combined with a deposit mechanism allows a signer to make conflict statements corresponding to a policy to be penalized. We present a generic construction of PoAPS based on Policy-Based Verifiable Secret Sharing (PBVSS) and demonstrate its practicality via a concrete implementation in the blockchain. Compared with the existing solutions that only handle specific types of equivocation, our proposed approach is more generic and can be instantiated to deal with various kinds of equivocation.

The proliferation of Internet of Things (IoT) devices has made people's lives more convenient, but it has also raised many security concerns. Due to the difficulty of obtaining and emulating IoT firmware, in the absence of internal execution information, black-box fuzzing of IoT devices has become a viable option. However, existing black-box fuzzers cannot form effective mutation optimization mechanisms to guide their testing processes, mainly due to the lack of feedback. In addition, because of the prevalent use of various and non-standard communication message formats in IoT devices, it is difficult or even impossible to apply existing grammar-based fuzzing strategies. Therefore, an efficient fuzzing approach with syntax inference is required in the IoT fuzzing domain.

Monero provides a high level of anonymity for both users and their transactions. However, many criminal activities might be committed with the protection of anonymity in cryptocurrency transactions. Thus, user accountability (or traceability) is also important in Monero transactions, which is unfortunately lacking in the current literature. In this paper, we fill this gap by introducing a new cryptocurrency named Traceable Monero to balance the user anonymity and accountability. Our framework relies on a tracing authority, but is optimistic, in that it is only involved when investigations in certain transactions are required. We formalize the system model and security model of Traceable Monero. We present a detailed construction of Traceable Monero by overlaying Monero with two types of tracing mechanisms, tracing the one-time addresses with money flows and tracing the long-term addresses. We prove the security of Traceable Monero and implement a prototype of the system, which demonstrates that Traceable Monero incurs merely a very small overhead in generating and verifying a transaction compared to Monero transactions.

Recent years have witnessed the proliferation of the deployment of virtualization techniques. Virtualization is designed to be transparent, that is, unprivileged users should not be able to detect whether a system is virtualized. Such detection can result in serious security threats such as evading virtual machine (VM)-based malware dynamic analysis and exploiting vulnerabilities for cross-VM attacks. The traditional software-based virtualization leaves numerous artifacts/fingerprints, which can be exploited without much effort to detect the virtualization. In contrast, current mainstream hardware-assisted virtualization significantly enhances the virtualization transparency, making itself more transparent and difficult to be detected. Nonetheless, we showcase three new identified low-level inconspicuous features, which can be leveraged by an unprivileged adversary to effectively and stealthily detect the hardware-assisted virtualization. All three features come from the chipset fingerprints, rather than the traces of software-based virtualization implementations (e.g., Xen or KVM). The identified features include i) Translation-Lookaside Buffer (TLB) stores an extra layer of address translations; ii) Last-Level Cache (LLC) caches one more layer of page-table entries; and iii) Level-1 Data (L1D) Cache is unstable. Based on the above features, we develop three corresponding virtualization detection techniques, which are then comprehensively evaluated on three native environments and three popular cloud providers: i) Amazon Elastic Compute Cloud, ii) Google Compute Engine and iii) Microsoft Azure. Experimental results validate that these three adversarial detection techniques are effective (with no false positive) and stealthy (without triggering suspicious system events, e.g., VM-exit) in detecting the above commodity virtualized environments.

The personalized Web service recommendation based on Quality of Service (QoS) is gaining increasing popularity due to its promising ability to help users find high quality services. Studies suggest that it is beneficial to use Collaborative Filtering (CF)-based techniques to facilitate Web service recommendations which can achieve high accuracy in predicting the QoS for unobserved Web services. With the QoS, location of users and Web services has been another significant factor in predicting the QoS values. The more factors that are available to the service providers, the more accurate predictions can be generated. However these factors are privacy sensitive and therefore it is risky to disclose them to any third party service provider. To address this challenge, in this paper we develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. Our protocol is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information. We conduct extensive experimental analysis on publicly available data sets and prove that our method is both secure and practical.

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhammer bypasses all of the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures.Recently, several proposals suggest exploiting the spatial proximity between the accessed memory location and the location of the bit flip for a defense against rowhammer. These all aim to deny the attacker's permission to access memory locations near sensitive data.In this paper, we question the core assumption underlying these defenses. We present PThammer, a confused-deputy attack that causes accesses to memory locations that the attacker is not allowed to access. Specifically, PThammer exploits the address translation process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. We implement PThammer, demonstrating that it is a viable attack, resulting in a system compromise (e.g., kernel privilege escalation). We further evaluate the effectiveness of proposed software-only defenses showing that PThammer can overcome those.

The Internet of Things (IoT) is transforming major industries, including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations, such as the amalgamation of software-defined networks (SDNs) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. The existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners' devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: 1) convolutional module (CNM); 2) randomization module; and 3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g., 91%-96%) with high model quality even under low privacy budgets (e.g., ε = 0.5).

Outlier detection is widely used in practice to track the anomaly on incremental data sets, such as network traffic and system logs. However, these data sets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this article, we present a privacy-preserving outlier detection (PPOD) protocol for incremental data sets. The protocol decomposes the outlier detection algorithm into several phases and recognizes the necessary cryptographic operations in each phase. It realizes several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted data sets. To support efficient updates, it integrates the sliding window model to periodically evict the expired data in order to maintain a constant update time. We build a prototype of PPOD and systematically evaluates the cryptographic modules and the overall protocols under various parameter settings. Our results show that PPOD can handle encrypted incremental data sets with a moderate computation and communication cost.

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.

We introduce MatRiCT, an efficient RingCT protocol for blockchain confidential transactions, whose security is based on "post-quantum'' (module) lattice assumptions. The proof length of the protocol is around two orders of magnitude shorter than the existing post-quantum proposal, and scales efficiently to large anonymity sets, unlike the existing proposal. Further, we provide the first full implementation of a post-quantum RingCT, demonstrating the practicality of our scheme. In particular, a typical transaction can be generated in a fraction of a second and verified in about 23 ms on a standard PC. Moreover, we show how our scheme can be extended to provide auditability, where a user can select a particular authority from a set of authorities to reveal her identity. The user also has the ability to select no auditing and all these auditing options may co-exist in the same environment. The key ingredients, introduced in this work, of MatRiCT are 1) the shortest to date scalable ring signature from standard lattice assumptions with no Gaussian sampling required, 2) a novel balance zero-knowledge proof and 3) a novel extractable commitment scheme from (module) lattices. We believe these ingredients to be of independent interest for other privacy-preserving applications such as secure e-voting. Despite allowing 64-bit precision for transaction amounts, our new balance proof, and thus our protocol, does not require a range proof on a wide range (such as 32- or 64-bit ranges), which has been a major obstacle against efficient lattice-based solutions. Further, we provide new formal definitions for RingCT-like protocols, where the real-world blockchain setting is captured more closely. The definitions are applicable in a generic setting, and thus are believed to contribute to the development of future confidential transaction protocols in general (not only in the lattice setting).

In this paper, we propose GraphSE\textsuperscript2, an encrypted graph database for online social network services to address massive data breaches. GraphSE\textsuperscript2 ~preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE\textsuperscript2 ~provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE\textsuperscript2 ~with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE\textsuperscript2 ~is practical for querying a social graph with a million of users.

Monero, ranked as one of the top privacy-preserving cryptocurrencies by market cap, introduced semi-annual hard fork in 2018. Although hard fork is not an uncommon event in the cryptocurrency industry, the two hard forks in 2018 caused an anonymity risk to Monero where transactions became traceable due to the problem of key reuse. Thisproblem was triggered by the existence of multiple copies of the same coin on different Monero blockchain branches such that the users spent the coins multiple times without preemptive action. We investigate the Monero hard fork events by analysing the transaction data on three different branches of the Monero blockchain. Although we have discovered an insignificant portion of traceable inputs compared to the total available inputs in our dataset, our analyses show that the scalability of the event depends on external factors such as market price and market availability. We propose a cheap, easy to implement strategy to prevent the problem of key reuse, should in the future stronger Monero forks emerge in the market.

The region of interest (ROI) of medical images in content-based image retrieval (CBIR) systems often require content authentication and verification. This is because adversarial modification of the stored image could have lethal effect on research, diagnostic outcome and the outcome of some forensic investigations. In this work, both robust watermarking and Fragile Steganography were combined with image search features to design a medical image retrieval system that incorporates ROI integrity verification. Original ROI features were pre-computed and embedded into archival images and utilised during retrieval for image integrity checks. The average global image PSNR was 38.36dB while the ROI PSNR was maintained at an average of 46dB with all watermark search features retrieved at zero bit error rate (BER) provided the attack on the image is not perceptible.

Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for fine-grained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access sub-policy, which is based on different patients' access policies. Then, the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, we prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.

The recently proposed Oblivious Cross-Tags (OXT) protocol (CRYPTO 2013) has broken new ground in designing efficient searchable symmetric encryption (SSE) protocol with support for conjunctive keyword search in a single-writer single-reader framework. While the OXT protocol offers high performance by adopting a number of specialised data-structures, it also trades-off security by leaking 'partial' database information to the server. Recent attacks have exploited similar partial information leakage to breach database confidentiality. Consequently, it is an open problem to design SSE protocols that plug such leakages while retaining similar efficiency. In this paper, we propose a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search. We avoid this leakage by adopting two additional cryptographic primitives - Hidden Vector Encryption (HVE) and probabilistic (Bloom filter) indexing into the HXT protocol. We propose a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations. At the same time, it affords selective simulation-security against an unbounded number of secret-key queries. Adopting this efficient HVE scheme, the overall practical storage and computational overheads of HXT over OXT are relatively small (no more than 10% for two keywords query, and 21% for six keywords query), while providing a higher level of security.

This paper presents a novel access control scheme for personal health record(PHR) data in cloud computing. The scheme utilizes attribute-based encryption(ABE), hash function and symmetric encryption to realize a fine-grained, multi- privilege access control to PHR. The patients can share their PHR with medical staff from various departments with different privileges securely. The experimental results show the efficiency of our scheme in terms of running-time, communication cost and storage overhead.

The Internet of Things (IoT) provides transparent and seamless incorporation of heterogeneous and different end systems. It has been widely used in many applications including smart cities such as public water system, power grid, water management, and vehicle traffic control system. In these smart city applications, a large number of IoT devices are deployed that can sense, communicate, compute, and potentially actuate. The uninterrupted and accurate functioning of these devices are critical to smart city applications as crucial decisions will be made based on the data received. One of the challenging tasks is to assure the authenticity of the devices so that we can rely on the decision making process with a very high confidence. One of the characteristics of IoT devices deployed in such applications is that they have limited battery power. A challenge is to design a secure mutual authentication protocol which is affordable to resource constrained devices. In this paper, we propose a lightweight mutual authentication protocol based on a novel public key encryption scheme for smart city applications. The proposed protocol takes a balance between the efficiency and communication cost without sacrificing the security. We evaluate the performance of our protocol in software and hardware environments. On the same security level, our protocol performance is significantly better than existing RSA and ECC based protocols. We also provide security analysis of the proposed encryption scheme and the mutual authentication protocol.

Encrypted data stored in clouds usually cannot be processed. To address this limitation, we design a practically efficient fully homomorphic encryption (FHE) scheme, which allows encrypted data to be directly processed by the clouds. Our scheme assumes that the clouds are curious to derive information from encrypted data, but not performing any nondeterministic brute-force attacks. We implemented a prototype of our scheme and evaluated its concrete performance by evaluating high-degree polynomials over encrypted data and calculating inner product of high-dimensional encrypted vectors.

Given a set of facilities F, a set of users U and a query facility q, a reverse k furthest neighbors (RkFN) query retrieves every user u ∈ U for which q is one of its k-furthest facilities. RkFN query is the natural complement of reverse k-nearest neighbors (RkNN) query that returns every user u for which q is one of its k-nearest facilities. While RkNN query returns the users that are highly influenced by a query q, RkFN query aims at finding the users that are least influenced by a query q. RkFN query has many applications in location-based services, marketing, facility location, clustering, and recommendation systems etc. While there exist several algorithms that answer RkFN query for k = 1, we are the first to propose a solution for arbitrary value of k. Based on several interesting observations, we present an efficient algorithm to process the RkFN queries. We also present a rigorous theoretical analysis to study various important aspects of the problem and our algorithm. An extensive experimental study is conducted using both real and synthetic data sets, demonstrating that our algorithm outperforms the state-of-the-art algorithm even for k = 1. The accuracy of our theoretical analysis is also verified by the experiments.

The requirement for data privacy is limiting to exploit the full potential of what modern data analytic capability could offer. To address such privacy concern, a number of techniques based on homomorphic encryption (HE) have been proposed to allow analytic computation, such as classification based on machine learning techniques, to run on encrypted data. However, these HE-based techniques suffer from a heavy computation overhead due to cryptographic computations having to be done on the encrypted data. We propose a non-colluding dual cloud system that utilizes Paillier cryptosystem. We illustrate how our proposal could reduce inherent computation overhead many similar techniques suffer. Such reduction could make our proposed system to be an ideal solution to use in the real world application.

The communication systems in existing smart grids mainly take the request/reply interaction model, in which data access is under the direct control of data producers. This tightly controlled interaction model is not scalable to support complex interactions among smart grid services. On the contrary, the publish/subscribe system features a loose coupling communication infrastructure and allows indirect, anonymous and multicast interactions among smart grid services. The publish/subscribe system can thus support scalable and flexible collaboration among smart grid services. However, the access is not under the direct control of data producers, it might not be easy to implement an access control scheme for a publish/subscribe system. In this article, we propose a Data-Centric Access Control Framework (DCACF) to support secure access control in a publish/subscribe model. This framework helps to build scalable smart grid services, while keeping features of service interactions and data confidentiality at the same time. The data published in our DCACF is encrypted with a fully homomorphic encryption scheme, which allows in-grid homomorphic aggregation of the encrypted data. The encrypted data is accompanied by bloom-filter encoded control policies and access credentials to enable indirect access control. We have analyzed the correctness and security of our DCACF and evaluated its performance in a distributed environment.

Establishing secure collaborations between multiple organisations, potentially who are in competitors, requires substantial careful attention to how information is exchanged during the collaboration, from the formulation of policies and agreements between the organisations that govern the collaboration at the most abstract level, through to authentication and authorisation services and down to secure network and storage infrastructure. This paper presents a high level description of the secure integrated collaboration platform for distributed groups that has been developed and deployed as a part of a pilot for the AU2EU project. This secure platform utilises advanced eAuthentication and eAuthorisation services integrated into an advanced real-time collaborative system offering high definition telepresence combined with a secure common shared workspace that gives capability based collaborative access to specialised instruments, data sets and images.

Clouds have the potentials to facilitate resource sharing for collaborations among different organizations or individuals. In this paper, we realise that the identity management scheme in current clouds is not very convenient for users to collaborate over clouds. This inconvenience is caused by the requirement to users who in order to access privately shared resources in clouds must register into the clouds, but might be reluctant to register. To address this inconvenience, we propose a user-controlled identity provisioning mechanism, by which a registered user can create local identities for collaborators to share the resources in his account, and using the local identities, collaborators can access the privately shared resources without being required to register. We demonstrate our mechanism by implementing a prototype. We believe our mechanism can benefit current clouds to improve their sharing services.

The trend towards Cloud computing infrastructure has increased the need for new methods that allow data owners to share their data with others securely taking into account the needs of multiple stakeholders. The data owner should be able to share confidential data while delegating much of the burden of access control management to the Cloud and trusted enterprises. The lack of such methods to enhance privacy and security may hinder the growth of cloud computing. In particular, there is a growing need to better manage security keys of data shared in the Cloud. BYOD provides a first step to enabling secure and efficient key management, however, the data owner cannot guarantee that the data consumers device itself is secure. Furthermore, in current methods the data owner cannot revoke a particular data consumer or group efficiently. In this paper, we address these issues by incorporating a hardware-based Trusted Platform Module (TPM) mechanism called the Trusted Extension Device (TED) together with our security model and protocol to allow stronger privacy of data compared to software-based security protocols. We demonstrate the concept of using TED for stronger protection and management of cryptographic keys and how our secure data sharing protocol will allow a data owner (e.g, author) to securely store data via untrusted Cloud services. Our work prevents keys to be stolen by outsiders and/or dishonest authorised consumers, thus making it particularly attractive to be implemented in a real-world scenario.

It is attractive for an organization to outsource its data analytics to a service provider who has powerful platforms and advanced analytics skills. However, the organization (data owner) may have concerns about the privacy of its data. In this paper, we present a method that allows the data owner to encrypt its data with a homomorphic encryption scheme and the service provider to perform k-means clustering directly over the encrypted data. However, since the ciphertexts resulting from homomorphic encryption do not preserve the order of distances between data objects and cluster centers, we propose an approach that enables the service provider to compare encrypted distances with the trapdoor information provided by the data owner. The efficiency of our method is validated by extensive experimental evaluation.

SUMMARY The database services on cloud are appearing as an attractive way of outsourcing databases. When a database is deployed on a cloud database service, the data security and privacy becomes a big concern for users. A straightforward way to address this concern is to encrypt the database. However, after encryption, the database cannot be easily queried. In this paper, we propose a nonlinear order preserving scheme for indexing encrypted data, which facilitates the range queries over encrypted databases. The scheme is secure even there are a large number of duplicates in plaintexts. Moreover, our scheme allows the programmability of basic indexing expressions and thus provides the capability of hiding the distribution of plaintexts from the distribution of indexes. This scheme is suitable for long‐standing databases because its use does not need any assumption on the characteristics of database data, such as their distribution, range and number, which may change dramatically over time.Copyright © 2013 John Wiley & Sons, Ltd.

The database services on cloud are appearing as an attractive way of outsourcing databases. When a database is deployed on a cloud database service, the data security and privacy becomes a big concern for users. A straightforward way to address this concern is to encrypt the database. However, an encrypted database cannot be easily queried. In this paper, we propose an order-preserving scheme for indexing encrypted data, which facilitates the range queries over encrypted databases. The scheme is secure since it randomizes each index with noises, such that the original data cannot be recovered from indexes. Moreover, our scheme allows the programmability of basic indexing expressions and thus the distribution of the original data can be hidden from the indexes.

The cloud database services are attractive for managing outsourced databases. However, the data security and privacy is a big concern hampering the acceptance of cloud database services. A straightforward way to address this concern is to encrypt the database, but an encrypted database cannot be easily queried.

Organizations of handling personal or sensitive information have the pressure of complying with relevant privacy laws or regulations. Since the laws or regulations are always written with complex legal terms, it is not easy for information system designers to understand precisely such laws and regulations and adopt them directly in their designs. In this paper, we propose the method of formalizing the Australian Privacy Act into executable processes and the method of modeling business in a privacy-aware way. Thus, by executing the processes over the privacy-aware business models, the information system designers can easily check the compliance of their designs with the privacy laws or regulations. In addition, the executable formalization of the Privacy Act makes it more efficient for law enforcement officers to process privacy violation cases. As an example, the clauses NPP 1.3 and NPP 2.1(s) of Australia Privacy Act are formalized and executed over a retailer's privacy-aware business model. The execution shows the same result as the investigation performed by the law enforcement officers.

We present a specification language Cloud# for modeling the internal organisation of cloud. By reasoning about cloud models, clients understand more on how services are delivered inside cloud. In this sense, cloud models make cloud services more transparent to clients. The transparency of cloud services are expected to increase the confidence of clients to move their business-critical applications to cloud. The expressiveness of Cloud# is evaluated by giving four cloud models, which demonstrate basic features of cloud computing, such as resource virtualization and air scheduling. We describe an application of Cloud# by building an architecture, in which Cloud# models are combined with remote attestation to deliver trusted services.

We report on our experiences in developing a hardware based security solution for a demonstration prototype of a novel, smart-grid enabled energy services delivery model. This model is based on establishing and maintaining trusted, secure dynamic collaborations with agreed, policy driven usage and b

We propose and implement a cloud architecture of virtual TPMs. In this architecture, TPM instances can be obtained from the TPM cloud on demand. Hence, the TPM functionality is available for applications that do not have TPM chips in their local platforms. Moreover, users can access their keys and data in the same TPM instance even if they move to other platforms. The TPM functionality in cloud is easy to access for applications developed in different languages since cloud computing delivers services in standard protocols. The functionality of the TPM cloud is demonstrated by using it to implement the Needham-Schroeder public-key protocol for web authentication.

The mechanism of establishing trust in a computing platform is tightly coupled with the characteristics of a specific machine. This limits the portability and mobility of trust as demanded by many emerging applications that go beyond the organizational boundaries. In order to address this problem, we propose a trusted computing platform in a form of a USB device. First, we describe the design and implementation of the hardware and software architectures of the device. We then demonstrate the capabilities of the proposed device by developing a trusted application.

Rigorously trusted services depend on reliable evidences to describe and check service behaviors. In this paper, we propose the pi-SOA framework, which delivers mutually trusted services in a rigorous way. The framework allows clients to verify service behaviors remotely according to their trust policies and uniquely identify the verified service at all times during its executions. On the other hand, service providers in this framework can check that clients have agreed with the service behaviors before using the service.

We propose data relevance query as a comprehensive way to help understand data relations built during execution of workflows. Correct data relation is critical for trusting workflow systems. The language RQL (Relevance Query Language) is designed for users to write relevance query declaratively. RQL combines Datalog into XQuery, such that it has the expressive control structures as XQuery and also has the deductive ability of Datalog. We use Java bytecode as a sample language to demonstrate how relevance facts can be captured. The captured relevance facts are then queried to understand how data are processed by bytecode programs.

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies.

This paper presents an approach to addressing the known weaknesses and security issues of JVM stack inspection in a unified framework. We first propose an enhanced JVM access control mechanism. In this mechanism, values are also associated with security levels. When enforcing access control, this mechanism checks not only the permissions of code on stack as the usual stack inspection, but also the security levels of values to make sure they are used legally. We then present a static type system to verify whether a bytecode program satisfies the security property achieved by this enhanced mechanism. This type system performs modular and context-sensitive analysis at the method level by generating and solving constraints, and path-sensitive analysis at the code block level by using a trace-based approach. In addition, this type system does not need any user annotation for verification

The metamodel techniques and model transformation techniques provide a standard way to represent and transform data, especially the software artifacts in software development. However, after a transformation is applied, the source model and the target model usually co-exist and evolve independently. How to propagate modifications across models in different formats still remains as an open problem.

XQuery is a powerful functional language to query XML data. This paper gives a bidirectional interpretation of XQuery to address the problem of updating XML data through materialized XQuery views. We first design an expressive bidirectional transformation language, and then translate XQuery expressions into the code of this language. As a result, an XQuery expression can execute in two directions: in the forward direction, it generates a materialized view from the source XML data; while in the backward direction, it updates the source data by putting back the updates on the view. we have implemented our approach and applied it to some XQuery use cases from a W3C draft, which confirms the practicability of this approach.

In the domain of XML authoring, there have been many tools to help users to edit XML documents. These tools make it easier to produce complex documents by using such technologies as syntax-directed or presentation-oriented editing, etc. However, when an XML document contains data with some computation dependency among them, these tools cannot free users from the burden of maintaining this dependency relationship. By computation dependency, we mean that some data are gotten by computing from other data in the same document.In this paper, we present an environment for authoring XML document, in which users can express the data dependency relationship in one document explicitly rather than implicitly in their minds. Under this environment, the dependent parts of the document are represented as expressions, which in turn can be evaluated to generate the dependent data. Therefore, users need not to compute the dependent data first and then input them manually, as required by the current authoring tools.