A Universally Composable Framework for the Privacy of Email Ecosystems

Email communication is amongst the most prominent online activities, and as such, can put sensitive information at risk. It is thus of high importance that internet email applications are designed in a privacy-aware manner and analyzed under a rigorous threat model. The Snowden revelations (2013) suggest that such a model should feature a global adversary, in light of the observational tools available. Furthermore, the fact that protecting metadata can be of equal importance as protecting the communication context implies that end-to-end encryption may be necessary, but it is not sufficient. With this in mind, we utilize the Universal Composability framework [Canetti, 2001] to introduce an expressive cryptographic model for email ``ecosystems\u27\u27 that can formally and precisely capture various well-known privacy notions (unobservability, anonymity, unlinkability, etc.), by parameterizing the amount of leakage an ideal-world adversary (simulator) obtains from the email functionality. Equipped with our framework, we present and analyze the security of two email constructions that follow different directions in terms of the efficiency vs. privacy tradeoff. The first one achieves optimal security (only the online/offline mode of the users is leaked), but it is mainly of theoretical interest; the second one is based on parallel mixing [Golle and Juels, 2004] and is more practical, while it achieves anonymity with respect to users that have similar amount of sending and receiving activity

Glass-Vault: A Generic Transparent Privacy-preserving Exposure Notification Analytics Platform

The highly transmissible COVID-19 disease is a serious threat to people’s health and life. To automate tracing those who have been in close physical contact with newly infected people and/or to analyse tracing-related data, researchers have proposed various ad-hoc programs that require being executed on users’ smartphones. Nevertheless, the existing solutions have two primary limitations: (1) lack of generality: for each type of analytic task, a certain kind of data needs to be sent to an analyst; (2) lack of transparency: parties who provide data to an analyst are not necessarily infected individuals; therefore, infected individuals’ data can be shared with others (e.g., the analyst) without their fine-grained and direct consent. In this work, we present Glass-Vault, a protocol that addresses both limitations simultaneously. It allows an analyst to run authorised programs over the collected data of infectious users, without learning the input data. Glass-Vault relies on a new variant of generic Functional Encryption that we propose in this work. This new variant, called DD-Steel, offers these two additional properties: dynamic and decentralised. We illustrate the security of both Glass-Vault and DD-Steel in the Universal Composability setting. Glass-Vault is the first UC-secure protocol that allows analysing the data of Exposure Notification users in a privacy-preserving manner. As a sample application, we indicate how it can be used to generate “infection heatmaps”

On the security of data markets: controlled Private Function Evaluation

The income of companies working on data markets steadily grows year by year. Private function evaluation (PFE) is a valuable tool in solving corresponding security problems. The task of Controlled Private Function Evaluation (CPFE) and its relaxed version (rCPFE) was proposed in [11]. We define an ideal functionality for the latter task and present a UC-secure realization of the functionality against static malicious parties. The core primitive is functional encryption (FE) and essentially this determines the conditions of realizability. Accordingly, in the case of non-adaptive FE-setting secure realization of the ideal functionality is achievable in the standard model, otherwise, accessibility of random oracle is required

On the Security of Data Markets and Private Function Evaluation

The income of companies working on data markets steadily grows year by year. Private function evaluation (PFE) is a valuable tool in solving corresponding security problems. The task of Controlled Private Function Evaluation and its relaxed version was introduced in [Horvath et.al., 2019]. In this article, we propose and examine several different approaches for such tasks with computational and information theoretical security against static corruption adversary. The latter level of security implies quantum-security. We also build known techniques and constructions into our solution where they fit into our tasks. The main cryptographic primitive, naturally related to the task is 1-out-of-n oblivious transfer. We use Secure Multiparty Computation techniques and in one of the constructions functional encryption primitive. The analysis of the computational complexity of the constructions shows that the considered tasks can efficiently be implemented, however it depends on the range of parameter values (e.g. size of database, size of the set of permitted function), the execution environment (e.g. concurrency) and of course on the level of security

Glass-Vault: A Generic Transparent Privacy-preserving Exposure Notification Analytics Platform

The highly transmissible COVID-19 disease is a serious threat to people’s health and life. To automate tracing those who have been in close physical contact with newly infected people and/or to analyse tracing-related data, researchers have proposed various ad-hoc programs that require being executed on users’ smartphones. Nevertheless, the existing solutions have two primary limitations: (1) lack of generality: for each type of analytic task, a certain kind of data needs to be sent to an analyst; (2) lack of transparency: parties who provide data to an analyst are not necessarily infected individuals; therefore, infected individuals’ data can be shared with others (e.g., the analyst) without their fine-grained and direct consent. In this work, we present Glass-Vault, a protocol that addresses both limitations simultaneously. It allows an analyst to run authorised programs over the collected data of infectious users, without learning the input data. Glass-Vault relies on a new variant of generic Functional Encryption that we propose in this work. This new variant, called DD-Steel, offers these two additional properties: dynamic and decentralised. We illustrate the security of both Glass-Vault and DD-Steel in the Universal Composability setting. Glass-Vault is the first UC-secure protocol that allows analysing the data of Exposure Notification users in a privacy-preserving manner. As a sample application, we indicate how it can be used to generate “infection heatmaps”

iUC: Flexible Universal Composability Made Simple

Proving the security of complex protocols is a crucial and very challenging task. A widely used approach for reasoning about such protocols in a modular way is universal composability. A perfect model for universal composability should provide a sound basis for formal proofs and be very flexible in order to allow for modeling a multitude of different protocols. It should also be easy to use, including useful design conventions for repetitive modeling aspects, such as corruption, parties, sessions, and subroutine relationships, such that protocol designers can focus on the core logic of their protocols. While many models for universal composability exist, including the UC, GNUC, and IITM models, none of them has achieved this ideal goal yet. As a result, protocols cannot be modeled faithfully and/or using these models is a burden rather than a help, often even leading to underspecified protocols and formally incorrect proofs. Given this dire state of affairs, the goal of this work is to provide a framework for universal composability which combines soundness, flexibility, and usability in an unmatched way. Developing such a security framework is a very difficult and delicate task, as the long history of frameworks for universal composability shows. We build our framework, called iUC, on top of the IITM model, which already provides soundness and flexibility while lacking sufficient usability. At the core of iUC is a single simple template for specifying essentially arbitrary protocols in a convenient, formally precise, and flexible way. We illustrate the main features of our framework with example functionalities and realizations

Understanding User-Perceived Security Risks and Mitigation Strategies in the Web3 Ecosystem

The advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3

Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization

Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union. In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation. To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions: First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems. This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization. Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces. Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts. The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways

Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

Identity and identification in an information society: Augmenting formal systems of identification with technological artefacts

Information and Communication Technology (ICT) are transforming society’s information flows. These new interactive environments decouple agents, information and actions from their original contexts and this introduces challenges when evaluating trustworthiness and intelligently placing trust.This thesis develops methods that can extend institutional trust into digitally enhanced interactive settings. By applying privacy-preserving cryptographic protocols within a technical architecture, this thesis demonstrates how existing human systems of identification that support institutional trust can be augmented with ICT in ways that distribute trust, respect privacy and limit the potential for abuse. Importantly, identification systems are located within a sociologically informed framework of interaction where identity is more than a collection of static attributes.A synthesis of the evolution and systematisation of cryptographic knowledge is presented and this is juxtaposed against the ideas developed within the digital identity community. The credential mechanism, first conceptualised by David Chaum, has matured into a number of well specified mathematical protocols. This thesis focuses on CL-RSA and BBS+, which are both signature schemes with efficient protocols that can instantiate a credential mechanism with strong privacy-preserving properties.The processes of managing the identification of healthcare professionals as they navigate their careers within the Scottish Healthcare Ecosystem provide a concrete case study for this work. The proposed architecture mediates the exchange of verifiable, integrity-assured evidence that has been cryptographically signed by relevant healthcare institutions, but is stored, managed and presented by the healthcare professionals to whom the evidence pertains.An evaluation of the integrity-assured transaction data produced by this architecture demonstrates how it could be integrated into digitally augmented identification processes, increasing the assurance that can be placed in these processes. The technical architecture is shown to be practical through a series of experiments run under realistic production-like settings.This work demonstrates that designing decentralised, standards-based, privacy-preserving identification systems for trusted professionals within highly assured social contexts can distribute institutionalised trust to trustworthy individuals and empower these individuals to interface with society’s increasingly socio-technical systems