Ilias Politis

Dimitrios Sygletos, Dimitra Papatsaroucha, Marios Choudetsanakis et al.

As privacy concerns in AI technologies continue to grow, Homomorphic Encryption (HE) offers a way to perform computations on encrypted data without the need of decryption during operations. However, HE is limited to addition and multiplication, making non-linear functions incompatible in their original form. This limitation has become more critical with the widespread use of Large Language Models (LLMs), where the non-linearity of activation functions such as the Rectified Linear Unit (ReLU) poses challenges for deployment in privacy-preserving Natural Language Processing (NLP) settings. This paper proposes a kernel-based approximation of ReLU, enabling its use within HE-constrained settings and thus contributing a critical step toward supporting privacy-preserving LLMs. A smooth kernel-based function, mimicking ReLU, is approximated using a second-degree polynomial, inspired by Jackson's theorem, to achieve low multiplicative depth. The proposed method is trained and assessed directly on token embeddings from pre-trained LLMs and evaluated in various scenarios, from simulated and tokenized data to deep learning and transformer models. Results show improved approximation fidelity, supporting the method's suitability for secure and privacy-preserving inference in various tasks.

Anna Angelogianni, Ilias Politis, Christos Xenakis

Unequivocally, a single man in possession of a strong password is not enough to solve the issue of security. Studies indicate that passwords have been subjected to various attacks, regardless of the applied protection mechanisms due to the human factor. The keystone for the adoption of more efficient authentication methods by the different markets is the trade-off between security and usability. To bridge the gap between user-friendly interfaces and advanced security features, the Fast Identity Online (FIDO) alliance defined several authentication protocols. Although FIDO's biometric-based authentication is not a novel concept, still daunts end users and developers, which may be a contributor factor obstructing FIDO's complete dominance of the digital authentication market. This paper traces the evolution of FIDO protocols, by identifying the technical characteristics and security requirements of the FIDO protocols throughout the different versions while providing a comprehensive study on the different markets (e.g., digital banking, social networks, e-government, etc.), applicability, ease of use, extensibility and future security considerations. From the analysis, we conclude that there is currently no dominant version of a FIDO protocol and more importantly, earlier FIDO protocols are still applicable to emerging vertical services.

John C. Polley, Ilias Politis, Christos Xenakis et al.

With the COVID-19 pandemic entering a second phase and vaccination strategies being applied by countries and governments worldwide, there is an increasing expectation by people to return to normal life. There is currently a debate about immunity passports, privacy, and the enablement of individuals to safely enter everyday social life, workplace, and travel. Such digital immunity passports and vaccination certificates should meet people's expectations for privacy while enabling them to present to 3rd party verifiers tamper-evident credentials. This paper provides a comprehensive answer to the technological, ethical and security challenges, by proposing an architecture that provides to individuals, employers, and government agencies, a digital, decentralized, portable, immutable, and non-refutable health status cryptographic proof. It can be used to evaluate the risk of allowing individuals to return to work, travel, and public life activities.