Secure architectures for pairing based public key cryptography

Along with the growing demand for cryptosystems in systems ranging from large servers to mobile devices, suitable cryptogrophic protocols for use under certain constraints are becoming more and more important. Constraints such as calculation time, area, efficiency and security, must be considered by the designer. Elliptic curves, since their introduction to public key cryptography in 1985 have challenged established public key and signature generation schemes such as RSA, offering more security per bit. Amongst Elliptic curve based systems, pairing based cryptographies are thoroughly researched and can be used in many public key protocols such as identity based schemes. For hardware implementions of pairing based protocols, all components which calculate operations over Elliptic curves can be considered. Designers of the pairing algorithms must choose calculation blocks and arrange the basic operations carefully so that the implementation can meet the constraints of time and hardware resource area. This thesis deals with different hardware architectures to accelerate the pairing based cryptosystems in the field of characteristic two. Using different top-level architectures the hardware efficiency of operations that run at different times is first considered in this thesis. Security is another important aspect of pairing based cryptography to be considered in practically Side Channel Analysis (SCA) attacks. The naively implemented hardware accelerators for pairing based cryptographies can be vulnerable when taking the physical analysis attacks into consideration. This thesis considered the weaknesses in pairing based public key cryptography and addresses the particular calculations in the systems that are insecure. In this case, countermeasures should be applied to protect the weak link of the implementation to improve and perfect the pairing based algorithms. Some important rules that the designers must obey to improve the security of the cryptosystems are proposed. According to these rules, three countermeasures that protect the pairing based cryptosystems against SCA attacks are applied. The implementations of the countermeasures are presented and their performances are investigated.

Leadership, fairness and gender: a practitioner's perspective

This Portfolio of Exploration outlines the course of learning by a practitioner on a personal leadership journey. It constructs a functional examination of ideas around leadership. It explores the belief system which underpins leadership practice and it also explores to what extent the increased participation of women can lead to a qualitatively different type of leadership. Different models of leadership are explored to find the model which can best fit with the author’s perception of leadership. The Women’s Intercultural Leadership Model is discussed. This model conceptualises leadership as the envisioning of oneself and others as potential leaders. It seeks to promote that recognition whilst at the same time encouraging personal transformation. The promotion of a values based leadership is examined. The value of fairness is examined as an element of functional leadership. The extent to which increasing the number of women in leadership can lead to a qualitatively different type of leadership is explored. Drucker’s assertion that every practice rests on theory is used to unearth the underlying assumptions of this reflective practitioner doctorate. Kegan’s Theory of Adult Mental Development and Subject-Object separation is used to explicitly examine knowledge construction. Myers Briggs Type Indicator (MBTI) and Assumption/Commitment/Behaviour/Outcome (ACBO) template are used to promote practice change.

Profiling side-channel attacks on cryptographic algorithms

Traditionally, attacks on cryptographic algorithms looked for mathematical weaknesses in the underlying structure of a cipher. Side-channel attacks, however, look to extract secret key information based on the leakage from the device on which the cipher is implemented, be it smart-card, microprocessor, dedicated hardware or personal computer. Attacks based on the power consumption, electromagnetic emanations and execution time have all been practically demonstrated on a range of devices to reveal partial secret-key information from which the full key can be reconstructed. The focus of this thesis is power analysis, more specifically a class of attacks known as profiling attacks. These attacks assume a potential attacker has access to, or can control, an identical device to that which is under attack, which allows him to profile the power consumption of operations or data flow during encryption. This assumes a stronger adversary than traditional non-profiling attacks such as differential or correlation power analysis, however the ability to model a device allows templates to be used post-profiling to extract key information from many different target devices using the power consumption of very few encryptions. This allows an adversary to overcome protocols intended to prevent secret key recovery by restricting the number of available traces. In this thesis a detailed investigation of template attacks is conducted, along with how the selection of various attack parameters practically affect the efficiency of the secret key recovery, as well as examining the underlying assumption of profiling attacks in that the power consumption of one device can be used to extract secret keys from another. Trace only attacks, where the corresponding plaintext or ciphertext data is unavailable, are then investigated against both symmetric and asymmetric algorithms with the goal of key recovery from a single trace. This allows an adversary to bypass many of the currently proposed countermeasures, particularly in the asymmetric domain. An investigation into machine-learning methods for side-channel analysis as an alternative to template or stochastic methods is also conducted, with support vector machines, logistic regression and neural networks investigated from a side-channel viewpoint. Both binary and multi-class classification attack scenarios are examined in order to explore the relative strengths of each algorithm. Finally these machine-learning based alternatives are empirically compared with template attacks, with their respective merits examined with regards to attack efficiency.