Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Current state of digital signal processing in myoelectric interfaces and related applications

This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.Peer reviewe

A reference architecture for cloud-edge meta-operating systems enabling cross-domain, data-intensive, ML-assisted applications: architectural overview and key concepts

Future data-intensive intelligent applications are required to traverse across the cloudto-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.For UPC authors: this research was funded by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant number PID2021-124463OB-100.Peer ReviewedPostprint (published version

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö