Multi-user (mu) security considers large-scale attackers that, given access to a number of cryptosystem instances, attempt to compromise at least one of them. We initiate the study of mu security of the so-called GGM tree that stems from the pseudorandom generator to pseudorandom function transformation of Goldreich, Goldwasser, and Micali, with a goal to provide references for its recently popularized use in applied cryptography. We propose a generalized model for GGM trees and analyze its mu prefix-constrained pseudorandom function security in the random oracle model. Our model allows to derive concrete bounds and improvements for various protocols, and we showcase on the Bitcoin-Improvement-Proposal standard Bip32 hierarchical wallets and function secret sharing protocols. In both scenarios, we propose improvements with better performance and concrete security bounds at the same time. Compared with the state-of-the-art designs, our SHACAL3 - and Keccak -p-based Bip32 variants reduce the communication cost of MPC-based implementations by 73.3% to 93.8%, whereas our AES -based function secret sharing substantially improves mu security while reducing computations by 50%.

Recent advances of consensus control have made it significant in multi-agent systems such as in distributed machine learning, distributed multi-vehicle cooperative systems. However, during its application it is crucial to achieve resilience and privacy; specifically, when there are adversary/faulty nodes in a general topology structure, normal agents can also reach consensus while keeping their actual states unobserved. In this article, we modify the state-of-the-art Q-consensus algorithm by introducing predefined noise or well-designed cryptography to guarantee the privacy of each agent state. In the former case, we add specified noise on agent state before it is transmitted to the neighbors and then gradually decrease the value of noise so the exact agent state cannot be evaluated. In the latter one, the Paillier cryptosystem is applied for reconstructing reward function in two consecutive interactions between each pair of neighboring agents. Therefore, multi-agent privacy-preserving resilient consensus (MAPPRC) can be achieved in a general topology structure. Moreover, in the modified version, we reconstruct reward function and credibility function so both convergence rate and stability of the system are improved. The simulation results indicate the algorithms’ tolerance for constant and/or persistent faulty agents as well as their protection of privacy. Compared with the previous studies that consider both resilience and privacy-preserving requirements, the proposed algorithms in this article greatly relax the topological conditions. At the end of the article, to verify the effectiveness of the proposed algorithms, we conduct two sets of experiments, i.e., a smart-car hardware platform consisting of four vehicles and a distributed machine learning platform containing 10 workers and a server.

The use of voice-controlled virtual assistants (VAs) is significant, and user numbers increase every year. Extensive use of VAs has provided the large, cash-rich technology companies who sell them with another way of consuming users’ data, providing a lucrative revenue stream. Whilst these companies are legally obliged to treat users’ information “fairly and responsibly,” artificial intelligence techniques used to process data have become incredibly sophisticated, leading to users’ concerns that a lack of clarity is making it hard to understand the nature and scope of data collection and use. There has been little work undertaken on a self-contained user awareness tool targeting VAs. PrivExtractor, a novel web-based awareness dashboard for VA users, intends to redress this imbalance of understanding between the data “processors” and the user. It aims to achieve this using the four largest VA vendors as a case study and providing a comparison function that examines the four companies’ privacy practices and their compliance with data protection law. As a result of this research, we conclude that the companies studied are largely compliant with the law, as expected. However, the user remains disadvantaged due to the ineffectiveness of current data regulation that does not oblige the companies to fully and transparently disclose how and when they use, share, or profit from the data. Furthermore, the software tool developed during the research is, we believe, the first that is capable of a comparative analysis of VA privacy with a visual demonstration to increase ease of understanding for the user.

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.

This article tackles the problem of designing efficient binary-level verification for a subset of information flow properties encompassing constant-time and secret-erasure . These properties are crucial for cryptographic implementations but are generally not preserved by compilers. Our proposal builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, Binsec/Rel , for bug-finding and bounded-verification of constant-time and secret-erasure and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach. Using Binsec/Rel , we also automate two prior manual studies on preservation of constant-time and secret-erasure by compilers for a total of 4,148 and 1,156 binaries, respectively. Interestingly, our analysis highlights incorrect usages of volatile data pointer for secret-erasure and shows that scrubbing mechanisms based on volatile function pointers can introduce additional register spilling that might break secret-erasure. We also discovered that gcc -O0 and backend passes of clang introduce violations of constant-time in implementations that were previously deemed secure by a state-of-the-art constant-time verification tool operating at LLVM level, showing the importance of reasoning at binary level.

Migrating enterprises and business capabilities to cloud platforms like Amazon Web Services (AWS) has become increasingly common. However, securing cloud operations, especially at large scales, can quickly become intractable. Customer-side issues such as service misconfigurations, data breaches, and insecure changes are prevalent. Furthermore, cloud-specific tactics and techniques paired with application vulnerabilities create a large and complex search space. Various solutions and modeling languages for cloud security assessments exist. However, no single one appeared sufficiently cloud-centered and holistic. Many also did not account for tactical security dimensions. This article, therefore, presents a domain-specific modeling language for AWS environments. When used to model AWS environments, manually or automatically, the language automatically constructs and traverses attack graphs to assess security. Assessments, therefore, require minimal security expertise from the user. The modeling language was primarily tested on four third-party AWS environments through securiCAD Vanguard, a commercial tool built around the AWS modeling language. The language was validated further by measuring performance on models provided by anonymous end users and a comparison with a similar open source assessment tool. As of March 2020, the modeling language could represent essential AWS structures, cloud tactics, and threats. However, the tests highlighted certain shortcomings. Data collection steps, such as planted credentials, and some missing tactics were obvious. Nevertheless, the issues covered by the DSL were already reminiscent of common issues with real-world precedents. Future additions to attacker tactics and addressing data collection should yield considerable improvements.

Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.

Smart contracts are tempting targets of attacks, as they often hold and manipulate significant financial assets, are immutable after deployment, and have publicly available source code, with assets estimated in the order of millions of dollars being lost in the past due to vulnerabilities. Formal verification is thus a necessity, but smart contracts challenge the existing highly efficient techniques routinely applied in the symbolic verification of software, due to specificities not present in general programming languages. A common feature of existing works in this area is the attempt to reuse off-the-shelf verification tools designed for general programming languages. This reuse can lead to inefficiency and potentially unsound results, as domain translation is required. In this article, we describe a carefully crafted approach that directly models the central aspects of smart contracts natively, going from the contract to its logical representation without intermediary steps. We use the expressive and highly automatable logic of constrained Horn clauses for modeling and instantiate our approach to the Solidity language. A tool implementing our approach, called Solicitous , was developed and integrated into the SMTChecker module of the Solidity compiler solc. We evaluated our approach on an extensive benchmark set containing 22,446 real-world smart contracts deployed on the Ethereum blockchain over a 27-month period. The results show that our approach is able to establish safety of significantly more contracts than comparable, publicly available verification tools, with an order of magnitude increase in the percentage of formally verified contracts.

Exciting recent advances in genome sequencing, coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Already today, one’s digitized DNA can be easily obtained from a sequencing lab and later used to conduct numerous tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and the often-proprietary nature of genomic tests, privacy is a natural and crucial issue. While genomic privacy received a great deal of attention within and outside the research community, genomic security has not been sufficiently studied. This is surprising since the usage of fake or altered genomes can have grave consequences, such as erroneous drug prescriptions and genetic test outcomes. Unfortunately, in the genomic domain, privacy and security (as often happens) are at odds with each other. In this article, we attempt to reconcile security with privacy in genomic testing by designing a novel technique for a secure and private genomic range query protocol between a genomic testing facility and an individual user. The proposed technique ensures authenticity and completeness of user-supplied genomic material while maintaining its privacy by releasing only the minimum thereof. To confirm its broad usability, we show how to apply the proposed technique to a previously proposed genomic private substring matching protocol. Experiments show that the proposed technique offers good performance and is quite practical. Furthermore, we generalize the genomic range query problem to sparse integer sets and discuss potential use cases.

Autonomous transport is receiving increasing attention, with research and development activities already providing prototype implementations. In this article we focus on Autonomous Passenger Ships (APS) , which are being considered as a solution for passenger transport across urban waterways. The ambition of the authors has been to examine the safety and security implications of such a Cyber Physical System (CPS) , particularly focusing on threats that endanger the passengers and the operational environment of the APS. Accordingly, the article presents a new risk assessment approach based on a Failure Modes Effects and Criticality Analysis (FMECA) that is enriched with selected semantics and components of the MITRE ATT&CK framework, in order to utilize the encoded common knowledge and facilitate the expression of attacks. Then, the proposed approach is demonstrated through conducting a risk assessment for a communication architecture tailored to the requirements of APSs that were proposed in earlier work. Moreover, we propose a group of graph theory-based metrics for estimating the impact of the identified risks. The use of this method has resulted in the identification of risks and their corresponding countermeasures, in addition to identifying risks with limited existing mitigation mechanisms. The benefits of the proposed approach are the comprehensive, atomic, and descriptive nature of the identified threats, which reduce the need for expert judgment, and the granular impact estimation metrics that reduce the impact of bias. All these features are provided in a semi-automated approach to reduce the required effort and collectively are argued to enrich the design-level risk assessment processes with an updatable industry threat model standard, namely ATT&CK.