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Transactions on Engineering and Computing Sciences (TECS) Vol 13, Issue 1, January - 2025
Services for Science and Education – United Kingdom
minimize the need for data sharing. Yet, solutions that effectively combine PPML and XAI to
meet the dual objectives of transparency and privacy are limited [3].
This paper addresses these gaps by introducing a unified framework that integrates XAI and
PPML for use in real-time systems. The proposed framework is designed to ensure that ML
models are both interpretable and privacy-conscious while maintaining the computational
efficiency needed for real-time applications.
The main contributions of this work include:
1. A novel framework that combines XAI and PPML to address the challenges of
transparency and privacy in real-time ML systems.
2. Application of the framework in real-world domains, such as healthcare and energy
management, to tackle pressing societal challenges.
3. A thorough evaluation that demonstrates improved interpretability and robust privacy
compliance without compromising system performance.
The paper is organized as follows: Section 2 discusses prior work related to XAI and PPML.
Section 3 explains the design and implementation of the proposed framework. Section 4
presents the experimental results and analysis. Section 5 delves into the broader implications
and limitations of the study. Finally, Section 6 concludes with key findings and
recommendations for future research.
RELATED WORK
Explainable Artificial Intelligence (XAI) and Privacy-Preserving Machine Learning (PPML) have
emerged as two critical areas of research, each addressing key challenges in modern machine
learning systems. While XAI seeks to enhance the transparency and interpretability of models,
PPML focuses on protecting data privacy during training and inference processes. Despite the
significant progress in both fields, there remains a scarcity of research combining these two
domains, particularly for real-time applications.
Explainable Artificial Intelligence (XAI)
The growing complexity of machine learning models has increased the demand for methods
that provide clear and understandable explanations of their predictions. Techniques such as
Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations
(SHAP) have become standard tools for post-hoc interpretability [4]. These methods generate
explanations after the model has been trained, but they often require additional computational
resources, which limits their utility in scenarios where rapid responses are necessary.
Additionally, post-hoc methods, while useful, do not inherently make the model itself
interpretable by design. This distinction is particularly important in domains like autonomous
systems or clinical decision-making, where both speed and trust are essential [5].
Privacy-Preserving Machine Learning (PPML)
In response to increasing concerns about data security and privacy, PPML techniques have been
developed to allow model training and inference without exposing sensitive data. Federated
learning enables decentralized data processing, keeping data localized while still contributing
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Rahmati, M. (2025). A Unified Framework for Explainable and Privacy-Preserving Machine Learning in Real-Time Decision-Making Systems.
Transactions on Engineering and Computing Sciences, 13(1). 51-60.
URL: http://dx.doi.org/10.14738/tecs.131.18186
to global model updates [6]. Complementary methods such as differential privacy and
homomorphic encryption further strengthen data protection by ensuring that individual
contributions cannot be easily reconstructed [7]. However, these techniques can introduce
latency and computational overhead, making their integration into time-critical systems
challenging. Moreover, their application often overlooks the need for interpretability, creating
a gap in the development of solutions that address both privacy and transparency [8].
Integration of XAI and PPML
Although XAI and PPML address different aspects of ethical AI, there has been limited
exploration of frameworks that combine the two. A few studies have proposed mechanisms for
producing interpretable outputs in privacy-preserving environments, such as within federated
learning systems, where local data remains protected [9]. These approaches, while promising,
are often tailored to specific use cases and fail to generalize to broader applications requiring
real-time decision-making capabilities. The lack of comprehensive frameworks that unify these
concepts represents a significant opportunity for innovation, particularly in high-stakes
domains where both privacy and interpretability are critical.
This paper seeks to fill this gap by presenting a framework that integrates XAI and PPML for
real-time systems. The proposed approach aims to deliver transparent and secure decision- making capabilities without sacrificing performance, addressing the pressing need for ethical
and efficient AI solutions.
METHODS
This section outlines the proposed framework, which combines Explainable Artificial
Intelligence (XAI) and Privacy-Preserving Machine Learning (PPML) into a single architecture
designed for real-time decision-making applications. The framework is composed of three
primary components, each aimed at balancing interpretability, privacy, and efficiency.
Framework Overview
The framework integrates three key elements:
1. Interpretable Model Design: Focuses on using models that provide transparent
decision-making processes, supported by post-hoc explanation tools for added clarity.
2. Federated Learning Architecture: Implements distributed training methods to keep
sensitive data localized while contributing to a shared global model.
3. Secure Computation Techniques: Incorporates privacy-preserving methods such as
homomorphic encryption and differential privacy to ensure secure data processing.
Together, these components enable the framework to address the dual challenges of
transparency and privacy while maintaining the performance required for real-time
applications.
Interpretable Model Design
The framework prioritizes inherently interpretable models for foundational transparency. For
example, decision trees and generalized additive models (GAMs) are selected for their
simplicity and clarity in classification tasks. When more complex architectures, such as neural