Fabien A. P. Petitcolas

Pradip Mainali, Ismini Psychoula, Fabien A. P. Petitcolas

In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statements with a decision rule in the condition. This way, ExMo naturally provides an explanation for a prediction using the decision rule that was triggered. ExMo uses a new approach to extract decision rules from the training data using term frequency-inverse document frequency (TF-IDF) features. With TF-IDF, decision rules with feature values that are more relevant to each class are extracted. Hence, the decision rules obtained by ExMo can distinguish the positive and negative classes better than the decision rules used in the existing Bayesian Rule List (BRL) algorithm, obtained using the frequent pattern mining approach. The paper also shows that ExMo learns a qualitatively better model than BRL. Furthermore, ExMo demonstrates that the textual explanation can be provided in a human-friendly way so that the explanation can be easily understood by non-expert users. We validate ExMo on several datasets with different sizes to evaluate its efficacy. Experimental validation on a real-world fraud detection application shows that ExMo is 20% more accurate than BRL and that it achieves accuracy similar to those of deep learning models.

Ismini Psychoula, Andreas Gutmann, Pradip Mainali et al.

The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.

Marc Joye, Fabien A. P. Petitcolas

The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. Several technical solutions are being proposed to address the issue. Hardware security modules in cloud data centres appear limited to enterprise customers due to their complexity, while general multi-party computation techniques require a large number of message exchanges. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i) only require additively homomorphic encryption algorithms, (ii) limit interactions to a mere request and response, and (iii) that can be used directly for important machine-learning algorithms such as logistic regression and SVM classification. The basic protocols are then extended and applied to feed-forward neural networks.

Pradip Mainali, Carlton Shepherd, Fabien A. P. Petitcolas

This paper proposes a new privacy-enhancing, context-aware user authentication system, ConSec, which uses a transformation of general location-sensitive data, such as GPS location, barometric altitude and noise levels, collected from the user's device, into a representation based on locality-sensitive hashing (LSH). The resulting hashes provide a dimensionality reduction of the underlying data, which we leverage to model users' behaviour for authentication using machine learning. We present how ConSec supports learning from categorical and numerical data, while addressing a number of on-device and network-based threats. ConSec is implemented subsequently for the Android platform and evaluated using data collected from 35 users, which is followed by a security and privacy analysis. We demonstrate that LSH presents a useful approach for context authentication from location-sensitive data without directly utilising plain measurements.

Paul Dunphy, Fabien A. P. Petitcolas

The emergence of distributed ledger technology (DLT) based upon a blockchain data structure, has given rise to new approaches to identity management that aim to upend dominant approaches to providing and consuming digital identities. These new approaches to identity management (IdM) propose to enhance decentralisation, transparency and user control in transactions that involve identity information; but, given the historical challenge to design IdM, can these new DLT-based schemes deliver on their lofty goals? We introduce the emerging landscape of DLT-based IdM, and evaluate three representative proposals: uPort; ShoCard; and Sovrin; using the analytic lens of a seminal framework that characterises the nature of successful IdM schemes.