Many formal security criteria in fields like secure compilation (e.g. full abstraction and robust hyperproperty preservation), cryptography (e.g. security games, reduction proofs, and universal composability), and information flow control share a fundamental underlying structure. This underlying structure and related concepts also appear in broader contexts like denotational semantics and computational expressiveness, yet their connections are often overlooked. This paper identifies and systematizes this structure through robust properties (criteria holding in any context), robust abstractions (ensuring preservation of robust properties from abstract to concrete systems) and the new notion of non-degrading abstractions, which guarantees that the concrete system only exhibits concrete violations that are also present in the corresponding abstract system, thereby ensuring no new violations are introduced by the abstraction. By unifying concepts from different areas into a common framework, we highlight existing but underexplored connections, provide new insights and general results, and introduce a valuable perspective for understanding and advancing formal security.

With the rise and advancements of technology, digital security is facing unprecedented challenges. Traditional security measures, such as strong credentials and multi-factor authentication (MFA), are no longer sufficient to protect digital assets from attacks and unauthorized access or to effectively verify user authenticity. Zero trust architecture (ZTA) has emerged as a solution in response to these issues. Leading organizations, including Google, Forrester, Palo Alto, and CISCO, have initiated strategies to design their own deployable ZTAs to offer the perks of ZT to other organizations. It operates on various principles such as removing implicit trust, continuously authenticating users, and de-perimeterization. It provides a top-notch defense against various major cyberattacks on organizations. This study offers a comprehensive and multi-dimensional synthesis of ZTAs. It also examines the principles and implementation strategies of various ZTAs, comparing them using multiple parameters to highlight their strengths and weaknesses. In addition to the synthesis of ZTAs, the work demonstrates its practical value with sector-specific examples to make it more relevant for real-world applications. Through a detailed analysis, the study identified potential research gaps and areas in existing research that require further investigation. These insights can guide future developments in digital security. The findings aim to help organizations select and implement the most effective ZT strategies to enhance their cybersecurity posture.

We present a decision procedure for verifying whether a protocol respects privacy goals, given a bound on the number of transitions. We consider multi message-analysis problems , where the intruder does not know exactly the structure of the messages but rather knows several possible structures and that the real execution corresponds to one of them. This allows for modeling a large class of security protocols, with standard cryptographic operators, non-determinism, branching and statefulness. Our first contribution is the definition of a decision procedure for a fragment of alpha-beta privacy. Moreover, we have implemented a prototype tool as a proof-of-concept and a first step towards automation. Our second contribution is to show that, for a class of protocols satisfying certain syntactic conditions, it is sound to restrict the intruder model to a typed model, where the intruder only sends well-typed messages. Our typing result holds for an unbounded number of transitions.

The widespread use of encryption protocols and increasing privacy demands have significantly increased encrypted traffic, creating new challenges for network monitoring and threat detection. Current methods struggle with diverse scenarios and distinguish between subtle traffic patterns within webpages of the same application. To address these challenges, we introduce ANT-ET, an end-to-end multimodal framework designed for fine-grained encrypted webpage traffic fingerprinting. ANT-ET leverages a transformer to model payload semantics and constructs a traffic interaction graph to capture both temporal and spatial characteristics of packet interactions. Additionally, ANT-ET incorporates a gradient reversal layer to improve generalization by facilitating domain-invariant feature learning across related webpages. Experimental results demonstrate ANT-ET’s superior performance compared to various baseline models, which were evaluated using a proprietary encrypted webpage traffic dataset and three public datasets. Ablation studies confirm the effectiveness of different framework components, while sensitivity and complexity analyses further validate ANT-ET’s robustness and flexibility.

The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is time-consuming. On the other hand, the use of EHR for medical research (e.g., development of precision medicine and diagnostics techniques) and optimisation of practices in healthcare organisations require the EHR to be analysed. To achieve that, they should be easily accessible without compromising the patient’s privacy. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desired security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under their own multi-level, fine-grained access policies. Analyses show that our system outperforms existing solutions in terms of computational complexity.

We present an approach to the proactive enforcement of provisions and obligations, suitable for building policy enforcement mechanisms that both prevent and cause system actions. Our approach encompasses abstract requirements for proactive policy enforcement, a system model describing how enforcement mechanisms interact with and control target systems, and concrete policy languages and associated enforcement mechanisms. As examples of policy languages, we consider finite automata and timed dynamic condition response (DCR) graphs. We use finite automata to illustrate the basic principles and DCR graphs to show how these principles can be adapted to a practical, real-time policy language. In both cases, we show how to algorithmically determine whether a given policy is enforceable and, when this is the case, construct an associated enforcement mechanism. Our approach improves upon existing formalisms in two ways: (1) we exploit the target system’s existing functionality to avert policy violations proactively, rather than compensate for them reactively; and (2) rather than requiring the manual specification of remedial actions in the policy, we deduce required actions directly from the policy.

The Internet of Things (IoT) has shown rapid growth in recent years. However, it presents challenges related to the lack of standardization of communication produced by different types of devices. Another problem area is the security and privacy of data generated by IoT devices. Thus, with the focus on grouping, analyzing, and classifying existing data security and privacy methods in IoT, based on data anonymization, we have conducted a Systematic Literature Review (SLR). We have therefore reviewed the history of works developing solutions for security and privacy in the IoT, particularly data anonymization and the leading technologies used by researchers in their work. We also discussed the challenges and future directions for research. The objective of the work is to give order to the main approaches that promise to provide or facilitate data privacy using anonymization in the IoT area. The study’s results can help us understand the best anonymization techniques to provide data security and privacy in IoT environments. In addition, the findings can also help us understand the limitations of existing approaches and identify areas for improvement. The results found in most of the studies analyzed indicate a lack of consensus in the following areas: (i) with regard to a solution with a standardized methodology to be applied in all scenarios that encompass IoT; (ii) the use of different techniques to anonymize the data; and (iii), the resolution of privacy issues. On the other hand, results made available by the k-anonymity technique proved efficient in combination with other techniques. In this context, data privacy presents one of the main challenges for broadening secure domains in applying privacy with anonymity.

There is an increasing number of cyber-systems (e.g., systems for payment, transportation, voting, critical infrastructures) whose security depends intrinsically on human users. In this paper, we introduce a novel approach for the formal and automated analysis of security ceremonies. A security ceremony expands a security protocol to include human nodes alongside computer nodes, with communication links that comprise user interfaces, human-to-human communication and transfers of physical objects that carry data, and thus a ceremony’s security analysis should include, in particular, the mistakes that human users might make when participating actively in the ceremony. Our approach defines mutation rules that model possible behaviors of a human user, automatically generates mutations in the behavior of the other agents of the ceremony to match the human-induced mutations, and automatically propagates these mutations through the whole ceremony. This allows for the analysis of the original ceremony specification and its possible mutations, which may include the way in which the ceremony has actually been implemented or could be implemented. To automate our approach, we have developed the tool X-Men, which is a prototype that builds on top of Tamarin, one of the most common tools for the automatic unbounded verification of security protocols. As a proof of concept, we have applied our approach to three real-life case studies, uncovering a number of concrete vulnerabilities. Some of these vulnerabilities were so far unknown, whereas others had so far been discovered only by empirical observation of the actual ceremony execution or by directly formalizing alternative models of the ceremony by hand, but X-Men instead allowed us to find them automatically.

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Non-interactive zero-knowledge proof or argument (NIZK) systems are widely used in many security sensitive applications to enhance computation integrity, privacy and scalability. In such systems, a prover wants to convince one or more verifiers that the result of a public function is correctly computed without revealing the (potential) private input, such as the witness. In this work, we introduce a new notion, called scriptable SNARK, where the prover and verifier(s) can specify the function (or language instance) to be proven via a script. We formalize this notion in UC framework and provide a generic trusted hardware based solution. We then instantiate our solution in both SGX and Trustzone with Lua script engine. The system can be easily used by typical programmers without any cryptographic background. The benchmark result shows that our solution is better than all the known SNARK proof systems w.r.t. prover’s running time (1000 times faster), verifier’s running time, and the proof size. In addition, we also give a lightweight scriptable SNARK protocol for hardware with limited state, e.g., Θ ( λ ) bits. Finally, we show how the proposed scriptable SNARK can be readily deployed to solve many well-known problems in the blockchain context, e.g. verifier’s dilemma, fast joining for new players, etc.