The rapid growth of machine learning (ML) technologies has raised significant concerns about their environmental impact, particularly regarding energy consumption and carbon emissions. This comprehensive review examines the intersection of ML and sustainability, synthesizing research from 2014 to 2024 to provide a holistic view of sustainable ML practices. This systematic review, encompassing over 200 peer-reviewed publications, reveals a growing emphasis on quantifying and mitigating the environmental footprint of ML systems. Key findings include: (1) a 300% increase in sustainable ML research since 2020; (2) the emergence of specialized carbon footprint quantification tools for ML; and (3) promising advancements in energy-efficient algorithms and green computing infrastructure. This research identifies critical challenges, including the lack of standardized sustainability metrics and the need for more robust life-cycle assessments of ML systems. The review also highlights the potential of transfer learning, federated learning, and hardware innovations in reducing ML's environmental impact. The analysis culminates in a novel framework for implementing sustainable practices in ML projects and a detailed roadmap for future research. This work provides researchers, practitioners, and policymakers with crucial insights to drive the development of more environmentally responsible ML technologies, ultimately contributing to global sustainability goals.

The synchrophasor technology has been changing the wide-area monitoring system (WAMS) of power grids; as it facilitates real-time grid situational awareness, observability and state estimation, and online stability assessment. However, the measurement samples inside the substations, or in phasor data concentrators (PDCs) can become a target for cyber-attacks, such as false data injection and data tampering attacks. Moreover, the synchrophasors are vulnerable to a single point of failure. Blockchain, the main technology behind the bitcoin, can simultaneously ensure reliability, security, and integrity with decentralization properties in a peer-to-peer system. Therefore, to mitigate the issues in the synchrophasor network, in this paper, we propose a blockchain-based synchrophasor network protection framework to enhance the self-defensive capability of its monitoring system. Our proposed model can mitigate data manipulation attacks from malicious entities by introducing bloom filter based fast phasor data identity validation, and Merkle tree-based fast data verification. Finally, the blockchain consensus can help to keep the consistency in the system and eliminate malicious entities from manipulating the overall security of the protection framework. Our experimental results show that the proposed system is scalable and fast in terms of synchrophasor data verification.

Due to the increasing demand for efficient, effective, and profitable applications of Artificial Intelligence (AI) in various industries, there is an immense need for professionals with the right skills to meet this demand. As a result, several institutions have started to offer AI programs. Yet, there is a notable gap in academia: the absence of a comprehensive, technically focused framework for developing a master’s program in Applied AI specifically tailored to meet the demands of the Computer and Information Systems (CIS) discipline. This paper addresses this gap by proposing a graduate-level Applied AI program framework for CIS students. This framework comprises an example graduate-level curriculum that focuses on critical competencies in the application of AI, including data analytics, machine learning, natural language processing, computer vision, AI ethics and governance, AI project management, IT infrastructure for AI systems, and AI-driven innovation. By following this framework, institutions can develop customized master’s programs that align with the evolving field of AI and prepare CIS graduates to excel in diverse industries.

The proliferation of GPS-enabled mobile devices has made location-based services (LBSs) very popular in mobile networks and protecting user's location information has become a critical issue of it. In a typical location-based service framework, the LBS requires the user to provide precise location information to assure better Quality of Services(QoS), while the user wants to hide this information as much as possible. This dilemma of privacy and quality of service trade-off has been studied in literature extensively. In this paper, we propose a spatial obfuscation framework, Privacy Preserving Voronoi Cell (PPVC) based on Voronoi diagram. In this framework user's region of interest (ROI) is divided into n sectors and each sector is concealed with an irregular quadrilateral, generated from a n-gon Voronoi cell and user's location is confounded within a convex region called Anonymity Zone. We evaluate PPVC with simulated environment, showing the efficiency and efficacy of the proposed framework. Finally, we implement it on Android phone with a user centric privacy scale.

The growing prevalence of adversarial attacks on machine learning models in consumer electronics necessitates enhancing adversarial robustness. Although adversarial training improves the robustness of a model against adversarial attacks, its sustainability remains a critical concern due to carbon emissions and the environmental impact of the extensive computational demands. To address this, we use the Robust Carbon Trade-Off Index metric, which establishes a relationship between robustness and carbon emissions, and introduce the Cost Per Unit of Robustness Change metric to quantify the economic impact of increasing robustness in terms of carbon emission costs measured by an economic metric quantifying the costs associated with carbon emissions. By examining the theoretical foundations, practical quantification techniques, and interdisciplinary research areas, we shed light on the multifaceted aspects of building sustainable and scalable models with robust adversarial defenses.

In Location-Based Services (LBS), users are required to disclose their precise location information to query a service provider. An untrusted service provider can abuse those queries to infer sensitive information on a user through spatio-temporal and historical data analyses. Depicting the drawbacks of existing privacy-preserving approaches in LBS, we propose a user-centric obfuscation approach, called KLAP, based on the three fundamental obfuscation requirements: k number of locations, l-diversity, and privacy area preservation. Considering user's sensitivity to different locations and utilizing Real-Time Traffic Information (RTTI), KLAP generates a convex Concealing Region (CR) to hide user's location such that the locations, forming the CR, resemble similar sensitivity and are resilient against a wide range of inferences in spatio-temporal domain. For the first time, a novel CR pruning technique is proposed to significantly improve the delay between successive CR submissions. We carry out an experiment with a real dataset to show its effectiveness for sporadic, frequent, and continuous service use cases.