The role of Signal Processing in Meeting Privacy Challenges [an overview]

International audienceWith the increasing growth and sophistication of information technology, personal information is easily accessible electronically. This flood of released personal data raises important privacy concerns. However, electronic data sources exist to be used and have tremendous value (utility) to their users and collectors, leading to a tension between privacy and utility. This article aims to quantify that tension by means of an information-theoretic framework and motivate signal processing approaches to privacy problems. The framework is applied to a number of case studies to illustrate concretely how signal processing can be harnessed to provide data privacy

Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive volumes of data explosion. The generated data is collected and shared using edge-based IoT structures at a considerably high frequency. Thus, the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes. To address the identified issue, we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme. In particular, we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes, where IoT devices and edge nodes are two parties of the game. IoT devices may make malicious requests to achieve their goals of stealing privacy. Accordingly, edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed. They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs. Built upon a developed application framework to illustrate the concrete data sharing architecture, a novel algorithm is proposed that can derive the optimal evolutionary learning strategy. Furthermore, we numerically simulate evolutionarily stable strategies, and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme. Therefore, the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared

Privacy-Preserved Linkable Social-Physical Data Publication

In this dissertation, we investigate the privacy-preserved data publication problems towards pervasively existing linkable social-physical contents. On the one hand, data publication has been considered as a critical approach to facilitate numerous utilities for individuals, populations, platform owners, and all third-party service providers. On the other hand, the unprecedented adoption of mobile devices and the dramatic development of Internet-of-Thing (IoT) systems have pushed the collection of surrounding physical information among populations to a totally novel stage. The collected contents can provide a fine-grained access to both physical and social aspects of the crowds, which introduces a comprehensively linkable and potentially sensitive information domain. The linkage includes the related index like privacy, utility, and efficiency for sophisticated applications, the inherent correlations among multiple data sources or information dimensions, and the connections among individuals. As the linkage leads to various novel challenges for privacy preservation, there should be a body of novel mechanisms for linkable social-physical data publications. As a result, this dissertation proposes a series of mechanisms for privacy-preserved linkable social-physical data publication. Firstly, we study the publication of physical data where the co-existing useful social proles and the sensitive physical proles of the data should be carefully maintained. Secondly, we investigate the data publication problem jointly considering the privacy preservation, data utility, and resource efficiency for task completion in crowd-sensing systems. Thirdly, we investigate the publication of private contents used for the recommendation, where contents of a user contribute to the recommendation results for others. Fourthly, we study the publications of reviews in local business service systems, where users expect to conceal their frequently visited locations while cooperatively maintain the utility of the whole system. Fifthly, we study the acquisition of privacy-preserved knowledge on cyber-physical social networks, where third-party service providers can derive the community structure without accessing the sensitive social links. We also provide detailed analysis and discussion for proposed mechanisms, and extensively validate their performance via real-world datasets. Both results demonstrate that the proposed mechanisms can properly preserve the privacy while maintaining the data utility. At last, we also propose the future research topics to complete the whole dissertation. The first topic focuses on the privacy preservation towards correlations beneath multiple data sources. The second topic studies more privacy issues for the whole population during data publication, including both the novel threats for related communities, and the disclosure of trends within crowds

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Opportunities and Challenges for Implementing Smart City Solutions in Finnish Municipalities : Viewpoint of Sustainable Transportation

Global trend of accelerated urbanization has caused challenges for the transportation systems of cities. A smart city’s smart transportation solutions are now thriving to solve different defects and develop the overall transportation system operation in cities. The smart transportation solutions have been considered as a robust part of developing the future transportation system in cities more efficient and sustainable. Internet of things has been a remarkable rendering factor on the development of these new smart solutions. The thesis examines the opportunities and challenges for implementing smart city solutions in Finnish municipalities from the viewpoint of sustainable transportation. The research question of the thesis is “What are the main opportunities and challenges for Finnish municipalities when implementing smart city solutions for sustainable transportation?” Conceptual framework of the thesis is based on literature of recent international studies which were systematically selected. The literature review consists of reviewing the concept of smart city and sustainability dimensions of it, concept of smart transportation and the sustainability concerns of it, and internet of things in the smart transportation. In addition, contemporary state of smart transportation development in Finnish municipalities is reviewed. Methodology of this thesis was conducted through semi-structured interviews with five different experts of smart transportation sector in Finland. The interviewees were from different operators and organizations which offered versatile and comprehensive view of the topic and gave high quality answers to the research question and complemented the research objectives. Based on this study, the contemporary state of smart transportation development in Finnish municipalities is still in tentative stage even though there has been implemented concrete solutions already. However, the overall atmosphere towards smart transportation development is affirmative and there is knowledge of the possible opportunities of utilizing different smart technologies. Main opportunities for implementing smart transportation solutions for Finnish municipalities were to promote a change in modal split, to promote a change of the power source of vehicles, and to make the city transportation more efficient and safer for citizens. The main challenges of implementing smart transportation were that the solutions need to be rather far developed before they are capable of functioning in the transportation ecosystem, to get these solutions fitted in to the budgets of municipalities, when assembling new sensors there can be difficulties to acquire a power source, and business models for the new solutions can be challenging to develop. Kaupungistumisen kiihtyvä maailmanlaajuinen trendi on aiheuttanut haasteita kaupunkien liikennöinnin järjestämiseen. Älykkäiden kaupunkien älykkäät liikenneratkaisut pyrkivät nyt ratkaisemaan erilaisia haasteita ja kehittämään liikennejärjestelmiä kaupungeissa. Älykkäitä liikennejärjestelmiä on luonnehdittu merkittäväksi tekijäksi, jotta tulevaisuuden kaupunkien liikennejärjestelmistä saadaan tehokkaampia ja kestävämpiä. Esineiden internet on ollut tärkeä osa älykkäiden ratkaisujen läpimurrossa ja nopeassa kehityksessä. Tämä tutkielma tutkii älykkään liikenteen ratkaisujen käyttöönoton mahdollisuuksia ja haasteita Suomen kunnissa. Tutkielman tutkimuskysymys on: ”Mitkä ovat pääasialliset mahdollisuudet ja haasteet Suomen kunnissa liittyen älykkään kaupungin ratkaisujen käyttöönottoon näkökulmana kestävä liikenne?” Tutkielman teoreettinen viitekehys perustuu kansainvälisiin akateemisiin tutkimuksiin, jotka koskevat älykästä kaupunkia ja sen kestävyyden ulottuvuuksia, älykästä liikennettä ja kestävyyteen liittyviä seikkoja ja esineiden internetiä. Sen lisäksi eri saatavilla olevien eri toimijoiden julkaisemien lähteiden perusteella on tutkittu Suomen kuntien älykkäisiin kaupunkeihin liittyvien ratkaisujen kehitystä ja älykkään liikenteen kehityksen nykytilaa. Tutkielman tutkimusosa koostuu puolistrukturoiduista haastatteluista, joissa on haasteltu viittä eri älykkään liikenteen asiantuntijaa eri sektoreilta. Koska haastateltavat olivat eri sektoreilta, saatiin tutkimustavoitteiden ja -kysymyksen kannalta monipuolinen ja kokonaisvaltainen katsanto. Tutkielman perusteella älykkään liikenteen kehityksen nykytila Suomen kunnissa on edelleen pitkälti kokeellinen, vaikkakin erilaisia ratkaisuja on jo otettu käyttöön monissa kunnissa. Yleinen ilmapiiri älykkäitä liikenneratkaisuja kohtaan on positiivinen ja kunnissa ollaan tietoisia älykkääseen liikenteeseen liittyvien ratkaisujen mahdollisuuksista. Pääasialliset mahdollisuudet älykkään liikenteen ratkaisujen käyttöönotossa oli tutkielman perustella niiden hyödyntäminen kulkumuotojakauman muuttamisessa, ajoneuvojen käyttövoimajakauman muuttamisessa ja liikennejärjestelmän kokonaisvaltaisessa kehittämisessä tehokkaammaksi ja turvallisemmaksi. Sen sijaan yksi pääasiallisista haasteista liittyi siihen, että uusien älykkään liikenteen ratkaisujen pitää olla melko pitkälle kehitettyjä, jotta ne voidaan ottaa osaksi liikennöinnin ekosysteemiä muun muassa eri lait ja standardit huomioiden. Lisäksi haasteiksi on koettu älykkään liikenteen ratkaisuihin vaadittavien resurssien sisällyttäminen osaksi kuntien budjetointia, uusien sensoreiden sähkön saanti ja uusien älykkäiden liikenneratkaisujen bisnesmallien rakentaminen toimivaksi

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy