Acoustic side-channel attacks (SCAs) use audio produced by a system to bypass traditional security measures to extract sensitive information. Human interface devices, such as keyboards, have been the focus of such attacks, however, computer mice are input devices that are currently in a research gap. This paper explores the security risks the emitted mouse sounds pose during usage. The methodology first establishes a proof of concept attack by classifying the mouse movement into up, down, left and right directions. The results lead to a 97% accuracy in distinguishing between the four categories in a controlled environment. This sets the stage by proving a leakage model useful for mouse acoustic SCAs. The research investigated the precision of tracking mouse movements by conducting experiments with ten unique movements on a large mouse pad. The study, using a dual-microphone setup, a smartphone in stereo recording, achieved 95% accuracy in discerning ten different movements. Furthermore, to place the research in a real-world context, the same experiment was repeated by adding two more directions (diagonal movement) and five other participants. The model was trained to become generalizable to six participants and 12 mouse pad movements, resulting in an accuracy of 94%. Given the same environment, this result shows the capability to extract sensitive information using a non-user-specific model. In addition, the paper experimented with a realistic attack scenario to infer a user action of closing a window on a laptop by clicking the red 'X' at the top right of the screen. The trained model could predict with 91% whether a mouse movement and click described the close window event. The experiments and findings within this research confirm audio leakage from a computer mouse in use. Moreover, the SCA poses a security risk in real-world scenarios, as it allows us to trace user activity in a realistic scenario. This work has explored the limits of single microphone use for SCAs and opened the door toward dual-microphone setup for future experiments.

The Internet of Things industry is expanding rapidly. However, many security breaches occur, and privacy is often at stake in traditional IoT networks. These centralized systems will not be able to cope with dynamically changing environments. In light of these risks, it is crucial to prevent and minimize the chances of attacks occurring. Researchers have attempted to use blockchain for IoT security to ensure data consistency and availability. Fully public decentralized solutions for IoT still face data breaches. On the other hand, machine learning models detect potential attacks to create an effective defense system. This paper surveys state-of-the-art works looking to integrate blockchain with machine learning to protect data management for the IoT. Before exploring the various implementations, an analysis of multiple surveys that dive deeper into such integrations is made; then, five different blockchain and machine learning integrations. Many papers need a complete security analysis, and the experiments are limited. From studying the relevant integrations, this article presents a new scheme to protect IoT data using the DQNSB consensus algorithm to train a global model by distributing machine learning tasks, leveraging transparency to guarantee security.

Authentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers focused on automated de-authentication systems, either as a stand-alone mechanism or as a result of continuous authentication failures. Unfortunately, no single approach offers security, privacy, and usability. Face-recognition methods, for example, may be suitable for security and usability, but they violate user privacy by continuously recording their actions and surroundings. In this work, we propose BLUFADER, a novel continuous authentication system that takes advantage of blurred face detection and recognition to fast, secure, and transparent de-authenticate users, preserving their privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection and recognition continuously. To evaluate BLUFADER's practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The de-authentication system was trained and evaluated using the former, while the latter was used to appraise the privacy and increase variance at training time. To guarantee the privacy-preserving effectiveness of the selected physical blurring filter, we show that state-of-the-art deblurring models are not able to revert our physical blur. Further, we demonstrate that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Moreover, BLUFADER effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. Last, our continuous authentication face recognition module based on Siamese Neural Network preventively protect users from adversarial attacks, enhancing the overall system security.

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Many regions are in urgent need of facial masks for slowing down the spread of COVID-19. To fight the pandemic, people are contributing masks through donation systems. Most existing systems are built on a centralized architecture which is prone to the single point of failure and lack of transparency. Blockchain-based solutions neglect fundamental privacy concerns (<italic>donation privacy</italic>) and security attacks (<italic>collusion attack, stealing attack</italic>). Moreover, current auditing solutions are not designed to achieve donation privacy, thus not appropriate in our context. In this work, we design a decentralized, anonymous, and secure auditing framework <italic>Astraea</italic> based on private smart contracts for donation systems. Specifically, we integrate a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute donations, prove the integrity of donation number (intention) and donation sum while preserving donation privacy. With DiSC, we design a Donation Smart Contract to refund deposits and defend against the stealing attack the collusion attack from malicious collector and transponder. We formally define and prove the privacy and security of Astraea by using security reduction. We build a prototype of Astraea to conduct extensive performance analysis. Experimental results demonstrate that Astraea is practically efficient in terms of both computation and communication.

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Double Auctions are mechanisms to trade commodities such as electricity or parts of the wireless spectrum at optimal prices. Bidders and sellers simultaneously submit quantity-price pairs to an auctioneer, denoting the quantity they want to buy or sell at specific prices. The auctioneer aggregates the offers into demand and supply curves to compute the auction result by finding the intersection between supply and demand. In this way, commodities exchange owners in an economically efficient manner, driven by the market. In an ideal scenario, the auctioneer is a trusted third party that does not abuse the information they gain. However, in reality, offers reveal usage patterns of customers, such as electricity usage, or may be used by the auctioneer for their economic gain as insider information. The auctioneer also has opportunities to manipulate results of which there are real-life allegations in electricity trading or advertisement auctions. These concerns call for solutions that conduct the auction in a privacy-preserving and verifiable way while not compromising the auction functionality or economic efficiency.

Proposed solutions for privacy-preserving and verifiable double auctions offer confidentiality but do not allow participants to verify results independently or vice versa without interaction of participants in the full auction procedure. We specifically focus on electricity trading to design a solution covering the above concerns. To the best of our knowledge, we propose the first privacy-preserving and verifiable double auction scheme without interactivity of all participants, tailored to electricity trading on (inter)national exchanges. Using cryptographic schemes, including Homomorphic encryption, Commitment schemes, and Zero-knowledge-proofs, we propose a solution to establish a double auction protocol that preserves privacy and allows for verification.

Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this article, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack, existing work requires heavy computations to execute user matching, and it is challenging for riders to verify matching results. Meanwhile, a malicious driver may cancel an assigned ride order due to its short distance. To address these issues, we present a RHS system named Nereus to support collusion resistance, efficiency, verifiability, and accountability. First, we integrate a smart contract into a Software Guard Extensions (SGX) enclave to establish a private smart contract for collusion resistance. We use a Bloom filter to achieve efficient matching. Second, we leverage privacy-preserving range query and Merkle proofs to make matching results verifiable. Meanwhile, we adopt short group signatures to provide anonymous authentication and deposit commitments to hold the runaway driver accountable. We formally state and prove the security and privacy of Nereus. We build a prototype based on Ethereum and SGX to conduct extensive performance analysis in regard to gas costs, computational costs, and communication overhead. Experimental results show that Nereus significantly improves over existing schemes in terms of computational costs.

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While encryption can prevent unauthorized access to a secret message, it does not provide undetectability of covert communications over the public network. Implementing a highly latent data exchange, especially with low eavesdropping/discovery probability, is challenging for practical scenarios, such as social and political movements in authoritarian regimes, military operations, and privacy preservation. Moreover, the current literature suffers from a low embedding capacity and monolingual applicability, limiting the amount of hiding secret data within short text messages using state-of-the-art algorithms, e.g., linguistic-based, structural-based, or coverless-based solutions. In this paper, we present a systematic covert communication technique called CovertSYS that enables a multilingual secure end-to-end conversation via messaging or social network platforms. The CovertSYS functions by encrypting a confidential message using a multi-factor authentication scheme and converting the encoded binary data into hidden Unicode symbols to be transmitted under cover of short text messages. We then conduct extensive experiments to confirm the security and validity of the proposed technique against state-of-the-art approaches. Our experimental results show that the CovertSYS provides a superior mean performance of 91.53% by improving the criteria scores: embedding capacity rate of 100%, imperceptibility rate of 76.4%, and distortion robustness rate of 98.2%. Finally, we discuss the practical implications of the proposed technique compared to the existing text steganography methods.@en

Voice messages are an increasingly well-known method of communication, accounting for more than 200 million messages a day. Sending audio messages requires a user to invest lesser effort compared to texting while enhancing the meaning of the message by adding an emotional context (e.g., irony). Unfortunately, we suspect that voice messages might provide much more information than intended. In fact, speech audio waves are both directly recorded by the microphone, as well as propagated into the environment and possibly reflected back to the microphone. Reflected waves along with ambient noise are also recorded by the microphone and sent as part of the voice message. In this thesis, we propose a novel attack for inferring detailed information about user location (e.g., a specific room) leveraging a simple WhatsApp voice message. We demonstrated our attack considering 7,200 voice messages from 15 different users and four environments (i.e., three bedrooms and a terrace). We considered three realistic attack scenarios depending on previous knowledge of the attacker about the victim and the environment. Our thorough experimental results demonstrate the feasibility and efficacy of our proposed attack. We can infer the location of the user among a pool of four known environments with 85% accuracy. Moreover, our approach reaches an average accuracy of 93% in discerning between two rooms of similar size and furniture (i.e., two bedrooms), and an accuracy of up to 99% in classifying indoor and outdoor environments.

We live in a world where much of our interactions with the environment around us depend on us being physically close to them. For instance, we have proximity­based tokens (e.g., keys and smartcards) for access systems installed at various places such as in cars, at contactless payment terminals, and in electronic passports. Moreover, such systems exist in critical environments like nuclear power plants.Unfortunately, the current systems used to detect proximity between devices and/or users are rife with vulnerabilities. Numerous attacks, such as Relay attack, Preamble Injection attack, Early Detect/Late Commit, and Cicada, exist that let an attacker maliciously alter the measured distance. The research community has proposed several solutions to address these problems and based on their inputs, the IEEE 802.15.4a standard was recently amended. Nevertheless, we show that the newer amendment(i.e., IEEE 802.15.4z) is however not entirely secure and still vulnerable to being exploited.In this work, we evaluate and address the vulnerabilities present in the recently introduced standard,IEEE 802.15.4z amendment for Ultra­Wide Band (UWB). This standard forms the basis of proximity detection in a majority of new devices such as keyfobs for cars, access control systems, smartphones like Samsung S21 and Google Pixel 6, and even medical equipment to monitor patients. First, we mount two attacks, namely the Cicada­TF and the Adaptive Injection, against UWB ­based proximity detection systems. Second, we propose a novel approach to detect the presence of these attacks. We create a real ­world testbed using DWM3000 ICs mounted on NRF52840­devkits to launch the attacks and implement our proposed detection approach. We evaluate the efficacy of our approach in three different environments: an indoor residence, a large outdoor passageway, and an office space. These environments were selected to represent the most commonly used places and were based on the802.15.4a channel models document by IEEE. Our experiment results show that the proposed model can detect the presence of attacks with high accuracy (94%) in all three environments. To the best of our knowledge, this is the first research work that presents a way to detect the presence of such attacks and also to be verified on hardware.

Deduplication is a widely used technology to reduce the storage and communication cost for cloud storage services. For any cloud infrastructure, data confidentiality is one of the primary concerns. Data confidentiality can be achieved via user-side encryption. However, conventional encryption mechanism is at odds with deduplication. Developing a user-side encryption mechanism with deduplication is a vital research topic. Existing state-of-the-art solutions in security of deduplication are vulnerable to dictionary attacks and tag inconsistency anomaly. In this paper, we present SPARK, a novel approach for secure pseudorandom key-based encryption for deduplicated storage. SPARK achieves semantic security along with deduplication. Security analysis proves that SPARK is secure against dictionary attacks and tag inconsistency anomaly. As a proof of concept, we implement SPARK in realistic environment and demonstrate its efficiency and effectiveness.

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