An effort allocation method to optimal code sanitization for quality-aware energy efficiency improvement

Abstract-Software energy efficiency has been shown to remarkably affect the energy consumption of IT platforms. Besides "performance" of the code in efficiently accomplishing a task, its "correctness" matters too. Software containing defects is likely to fail and the computational cost to complete an operation becomes much higher if the user encounters a failure. Both performancerelated energy efficiency of software and its defectiveness are impacted by the quality of the code. Exploiting the relation between code quality and energy/defectiveness attributes is the main idea behind this position paper. Starting from the authors' previous experience in this field, we define a method to first predict the applications of a software system more likely to impact energy consumption and with higher residual defectiveness, and then to exploit the prediction for optimally scheduling the effort for code sanitization -thus supporting, by quantitative figures, the quality assurance teams' decision-makers

Cybersecurity issues in software architectures for innovative services

The recent advances in data center development have been at the basis of the widespread success of the cloud computing paradigm, which is at the basis of models for software based applications and services, which is the "Everything as a Service" (XaaS) model. According to the XaaS model, service of any kind are deployed on demand as cloud based applications, with a great degree of flexibility and a limited need for investments in dedicated hardware and or software components. This approach opens up a lot of opportunities, for instance providing access to complex and widely distributed applications, whose cost and complexity represented in the past a significant entry barrier, also to small or emerging businesses. Unfortunately, networking is now embedded in every service and application, raising several cybersecurity issues related to corruption and leakage of data, unauthorized access, etc. However, new service-oriented architectures are emerging in this context, the so-called services enabler architecture. The aim of these architectures is not only to expose and give the resources to these types of services, but it is also to validate them. The validation includes numerous aspects, from the legal to the infrastructural ones e.g., but above all the cybersecurity threats. A solid threat analysis of the aforementioned architecture is therefore necessary, and this is the main goal of this thesis. This work investigate the security threats of the emerging service enabler architectures, providing proof of concepts for these issues and the solutions too, based on several use-cases implemented in real world scenarios

Variable autonomy assignment algorithms for human-robot interactions.

As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction has grown into an increasingly important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. However, such systems are often proscribed only at the most abstract level, with the meticulous details of implementation left to the designer\u27s prerogative. Without a rigorous structure for implementing controls, the work of design is frequently left to ad hoc mechanism with only bespoke guarantees of systematic efficacy, if any such proof is forthcoming at all. Herein, I present two quantitatively defined models for implementing sliding-scale variable autonomy, in which levels of autonomy are determined by the relative efficacy of autonomous subroutines. I experimentally test the resulting Variable Autonomy Planning (VAP) algorithm and against a traditional traded control scheme in a pick-and-place task, and apply the Variable Autonomy Tasking algorithm to the implementation of a robot performing a complex sanitation task in real-world environs. Results show that prioritizing autonomy levels with higher success rates, as encoded into VAP, allows users to effectively and intuitively select optimal autonomy levels for efficient task completion. Further, the Pareto optimal design structure of the VAP+ algorithm allows for significant performance improvements to be made through intervention planning based on systematic input determining failure probabilities through sensorized measurements. This thesis describes the design, analysis, and implementation of these two algorithms, with a particular focus on the VAP+ algorithm. The core conceit is that they are methods for rigorously defining locally optimal plans for traded control being shared between a human and one or more autonomous processes. It is derived from an earlier algorithmic model, the VAP algorithm, developed to address the issue of rigorous, repeatable assignment of autonomy levels based on system data which provides guarantees on basis of the failure-rate sorting of paired autonomous and manual subtask achievement systems. Using only probability ranking to define levels of autonomy, the VAP algorithm is able to sort modules into optimizable ordered sets, but is limited to only solving sequential task assignments. By constructing a joint cost metric for the entire plan, and by implementing a back-to-front calculation scheme for this metric, it is possible for the VAP+ algorithm to generate optimal planning solutions which minimize the expected cost, as amortized over time, funds, accuracy, or any metric combination thereof. The algorithm is additionally very efficient, and able to perform on-line assessments of environmental changes to the conditional probabilities associated with plan choices, should a suitable model for determining these probabilities be present. This system, as a paired set of two algorithms and a design augmentation, form the VAP+ algorithm in full

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Contribution to privacy-enhancing tecnologies for machine learning applications

For some time now, big data applications have been enabling revolutionary innovation in every aspect of our daily life by taking advantage of lots of data generated from the interactions of users with technology. Supported by machine learning and unprecedented computation capabilities, different entities are capable of efficiently exploiting such data to obtain significant utility. However, since personal information is involved, these practices raise serious privacy concerns. Although multiple privacy protection mechanisms have been proposed, there are some challenges that need to be addressed for these mechanisms to be adopted in practice, i.e., to be “usable” beyond the privacy guarantee offered. To start, the real impact of privacy protection mechanisms on data utility is not clear, thus an empirical evaluation of such impact is crucial. Moreover, since privacy is commonly obtained through the perturbation of large data sets, usable privacy technologies may require not only preservation of data utility but also efficient algorithms in terms of computation speed. Satisfying both requirements is key to encourage the adoption of privacy initiatives. Although considerable effort has been devoted to design less “destructive” privacy mechanisms, the utility metrics employed may not be appropriate, thus the wellness of such mechanisms would be incorrectly measured. On the other hand, despite the advent of big data, more efficient approaches are not being considered. Not complying with the requirements of current applications may hinder the adoption of privacy technologies. In the first part of this thesis, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, evaluated over original test data. Our experiments show that the impact of the de facto microaggregation standard on the performance of machine-learning algorithms is often minor for a variety of data sets. Furthermore, experimental evidence suggests that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data. Secondly, we address the problem of preserving the empirical utility of data. By transforming the original data records to a different data space, our approach, based on linear discriminant analysis, enables k-anonymous microaggregation to be adapted to the application domain of data. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction, penalizing distortion across the classification threshold. As a result, data utility is preserved in terms of the accuracy of machine learned models for a number of standardized data sets. Afterwards, we propose a mechanism to reduce the running time for the k-anonymous microaggregation algorithm. This is obtained by simplifying the internal operations of the original algorithm. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, this remarkable speedup factor is achieved with no additional loss of data utility.Les aplicacions de big data impulsen actualment una accelerada innovació aprofitant la gran quantitat d’informació generada a partir de les interaccions dels usuaris amb la tecnologia. Així, qualsevol entitat és capaç d'explotar eficientment les dades per obtenir utilitat, emprant aprenentatge automàtic i capacitats de còmput sense precedents. No obstant això, sorgeixen en aquest escenari serioses preocupacions pel que fa a la privacitat dels usuaris ja que hi ha informació personal involucrada. Tot i que s'han proposat diversos mecanismes de protecció, hi ha alguns reptes per a la seva adopció en la pràctica, és a dir perquè es puguin utilitzar. Per començar, l’impacte real d'aquests mecanismes en la utilitat de les dades no esta clar, raó per la qual la seva avaluació empírica és important. A més, considerant que actualment es manegen grans volums de dades, una privacitat usable requereix, no només preservació de la utilitat de les dades, sinó també algoritmes eficients en temes de temps de còmput. És clau satisfer tots dos requeriments per incentivar l’adopció de mesures de privacitat. Malgrat que hi ha diversos esforços per dissenyar mecanismes de privacitat menys "destructius", les mètriques d'utilitat emprades no serien apropiades, de manera que aquests mecanismes de protecció podrien estar sent incorrectament avaluats. D'altra banda, tot i l’adveniment del big data, la investigació existent no s’enfoca molt en millorar la seva eficiència. Lamentablement, si els requisits de les aplicacions actuals no es satisfan, s’obstaculitzarà l'adopció de tecnologies de privacitat. A la primera part d'aquesta tesi abordem el problema de mesurar l'impacte de la microagregació k-Gnónima en la utilitat empírica de microdades. Per això, quantifiquem la utilitat com la precisió de models de classificació obtinguts a partir de les dades microagregades. i avaluats sobre dades de prova originals. Els experiments mostren que l'impacte de l’algoritme de rmicroagregació estàndard en el rendiment d’algoritmes d'aprenentatge automàtic és usualment menor per a una varietat de conjunts de dades avaluats. A més, l’evidència experimental suggereix que la mètrica tradicional de distorsió de les dades seria inapropiada per avaluar la utilitat empírica de dades microagregades. Així també estudiem el problema de preservar la utilitat empírica de les dades a l'ésser anonimitzades. Transformant els registres originaIs de dades en un espai de dades diferent, el nostre enfocament, basat en anàlisi de discriminant lineal, permet que el procés de microagregació k-anònima s'adapti al domini d’aplicació de les dades. Per això, primer, les dades són rotades o projectades en la direcció de màxima discriminació i, segon, escalades en aquesta direcció, penalitzant la distorsió a través del llindar de classificació. Com a resultat, la utilitat de les dades es preserva en termes de la precisió dels models d'aprenentatge automàtic en diversos conjunts de dades. Posteriorment, proposem un mecanisme per reduir el temps d'execució per a la microagregació k-anònima. Això s'aconsegueix simplificant les operacions internes de l'algoritme escollit Mitjançant una extensa experimentació sobre diversos conjunts de dades, vam mostrar que el nou algoritme és bastant més ràpid. Aquesta acceleració s'aconsegueix sense que hi ha pèrdua en la utilitat de les dades. Finalment, en un enfocament més aplicat, es proposa una eina de protecció de privacitat d'individus i organitzacions mitjançant l'anonimització de dades sensibles inclosos en logs de seguretat. Es dissenyen diferents mecanismes d'anonimat per implementar-los en base a la definició d'una política de privacitat, en el context d'un projecte europeu que té per objectiu construir un sistema de seguretat unificat

Contribution to privacy-enhancing tecnologies for machine learning applications

For some time now, big data applications have been enabling revolutionary innovation in every aspect of our daily life by taking advantage of lots of data generated from the interactions of users with technology. Supported by machine learning and unprecedented computation capabilities, different entities are capable of efficiently exploiting such data to obtain significant utility. However, since personal information is involved, these practices raise serious privacy concerns. Although multiple privacy protection mechanisms have been proposed, there are some challenges that need to be addressed for these mechanisms to be adopted in practice, i.e., to be “usable” beyond the privacy guarantee offered. To start, the real impact of privacy protection mechanisms on data utility is not clear, thus an empirical evaluation of such impact is crucial. Moreover, since privacy is commonly obtained through the perturbation of large data sets, usable privacy technologies may require not only preservation of data utility but also efficient algorithms in terms of computation speed. Satisfying both requirements is key to encourage the adoption of privacy initiatives. Although considerable effort has been devoted to design less “destructive” privacy mechanisms, the utility metrics employed may not be appropriate, thus the wellness of such mechanisms would be incorrectly measured. On the other hand, despite the advent of big data, more efficient approaches are not being considered. Not complying with the requirements of current applications may hinder the adoption of privacy technologies. In the first part of this thesis, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, evaluated over original test data. Our experiments show that the impact of the de facto microaggregation standard on the performance of machine-learning algorithms is often minor for a variety of data sets. Furthermore, experimental evidence suggests that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data. Secondly, we address the problem of preserving the empirical utility of data. By transforming the original data records to a different data space, our approach, based on linear discriminant analysis, enables k-anonymous microaggregation to be adapted to the application domain of data. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction, penalizing distortion across the classification threshold. As a result, data utility is preserved in terms of the accuracy of machine learned models for a number of standardized data sets. Afterwards, we propose a mechanism to reduce the running time for the k-anonymous microaggregation algorithm. This is obtained by simplifying the internal operations of the original algorithm. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, this remarkable speedup factor is achieved with no additional loss of data utility.Les aplicacions de big data impulsen actualment una accelerada innovació aprofitant la gran quantitat d’informació generada a partir de les interaccions dels usuaris amb la tecnologia. Així, qualsevol entitat és capaç d'explotar eficientment les dades per obtenir utilitat, emprant aprenentatge automàtic i capacitats de còmput sense precedents. No obstant això, sorgeixen en aquest escenari serioses preocupacions pel que fa a la privacitat dels usuaris ja que hi ha informació personal involucrada. Tot i que s'han proposat diversos mecanismes de protecció, hi ha alguns reptes per a la seva adopció en la pràctica, és a dir perquè es puguin utilitzar. Per començar, l’impacte real d'aquests mecanismes en la utilitat de les dades no esta clar, raó per la qual la seva avaluació empírica és important. A més, considerant que actualment es manegen grans volums de dades, una privacitat usable requereix, no només preservació de la utilitat de les dades, sinó també algoritmes eficients en temes de temps de còmput. És clau satisfer tots dos requeriments per incentivar l’adopció de mesures de privacitat. Malgrat que hi ha diversos esforços per dissenyar mecanismes de privacitat menys "destructius", les mètriques d'utilitat emprades no serien apropiades, de manera que aquests mecanismes de protecció podrien estar sent incorrectament avaluats. D'altra banda, tot i l’adveniment del big data, la investigació existent no s’enfoca molt en millorar la seva eficiència. Lamentablement, si els requisits de les aplicacions actuals no es satisfan, s’obstaculitzarà l'adopció de tecnologies de privacitat. A la primera part d'aquesta tesi abordem el problema de mesurar l'impacte de la microagregació k-Gnónima en la utilitat empírica de microdades. Per això, quantifiquem la utilitat com la precisió de models de classificació obtinguts a partir de les dades microagregades. i avaluats sobre dades de prova originals. Els experiments mostren que l'impacte de l’algoritme de rmicroagregació estàndard en el rendiment d’algoritmes d'aprenentatge automàtic és usualment menor per a una varietat de conjunts de dades avaluats. A més, l’evidència experimental suggereix que la mètrica tradicional de distorsió de les dades seria inapropiada per avaluar la utilitat empírica de dades microagregades. Així també estudiem el problema de preservar la utilitat empírica de les dades a l'ésser anonimitzades. Transformant els registres originaIs de dades en un espai de dades diferent, el nostre enfocament, basat en anàlisi de discriminant lineal, permet que el procés de microagregació k-anònima s'adapti al domini d’aplicació de les dades. Per això, primer, les dades són rotades o projectades en la direcció de màxima discriminació i, segon, escalades en aquesta direcció, penalitzant la distorsió a través del llindar de classificació. Com a resultat, la utilitat de les dades es preserva en termes de la precisió dels models d'aprenentatge automàtic en diversos conjunts de dades. Posteriorment, proposem un mecanisme per reduir el temps d'execució per a la microagregació k-anònima. Això s'aconsegueix simplificant les operacions internes de l'algoritme escollit Mitjançant una extensa experimentació sobre diversos conjunts de dades, vam mostrar que el nou algoritme és bastant més ràpid. Aquesta acceleració s'aconsegueix sense que hi ha pèrdua en la utilitat de les dades. Finalment, en un enfocament més aplicat, es proposa una eina de protecció de privacitat d'individus i organitzacions mitjançant l'anonimització de dades sensibles inclosos en logs de seguretat. Es dissenyen diferents mecanismes d'anonimat per implementar-los en base a la definició d'una política de privacitat, en el context d'un projecte europeu que té per objectiu construir un sistema de seguretat unificat.Postprint (published version

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Advancements and Challenges in IoT Simulators: A Comprehensive Review

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term “Internet of Things”, which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators’ capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain