Conference PaperPDF Available

Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications

Authors:

Abstract

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases. A use case of edge AI for ECG feature extraction and real-time support of multiple sensor nodes is analyzed in the experiments.
Edge AI and Blockchain for
Privacy-Critical and Data-Sensitive Applications
A. Nawaz1,2, T. N. Gia2, J. Pe˜
na Queralta2and T. Westerlund2
1Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, China
2Turku Intelligent and Embedded Robotic Systems (TIERS), University of Turku, Finland
Emails: 1{nanum18, hbkan}@fudan.edu.cn, 2{jopequ, tunggi, tovewe}@utu.fi
Abstract—The edge and fog computing paradigms enable
more responsive and smarter systems without relying on cloud
servers for data processing and storage. This reduces network
load as well as latency. Nonetheless, the addition of new layers
in the network architecture increases the number of security
vulnerabilities. In privacy-critical systems, the appearance of
new vulnerabilities is more significant. To cope with this issue,
we propose and implement an Ethereum Blockchain based
architecture with edge artificial intelligence to analyze data at the
edge of the network and keep track of the parties that access the
results of the analysis, which are stored in distributed databases.
Index Terms—Blockchain; Edge Computing; AI; E-Health;
U-Health; IoT; Internet of Things; Fall Detection; Ubiquitous
Health; Ethereum;
I. INTRODUCTION
Users and organizations are becoming increasingly aware
of the importance and significance of protecting personal data
and online privacy. This is a particularly critical issue in the
IoT, where numerous security challenges have been identified
by the research community. In recent years, a wide variety
of IoT platforms and applications have adopted the use of
blockchain technology to mitigate multiple privacy risks and
allow secure transactions without the need for a trusted party.
Nevertheless, current integrations of blockchain within the
IoT have been focusing on securing communication without
changing the interaction topology. Exploiting the fog and
edge computing paradigms, we propose an extension of the
Ethereum blockchain to resource-constrained devices. With
our proposed platform, end-devices can negotiate directly with
third parties regarding the use of their data. This ensures
data owners are always aware of transactions involving their
data. In addition, because of the immutable nature of the
blockchain, all transactions are recorded and auditable, which
further reduces the possibilities of misuse of private data. We
have implemented and validated the proposed platform in a
real application, demonstrating its potential for integration of
IoT devices with scarce computational capabilities. To enhance
privacy-critical systems, edge based AI techniques has been
implemented to restrict raw data to its producers only. But,
this domain still lacks the owner control over their sensitive
health data, where owner can process sensitive information
by using neural networks and sell statistics to the interested
clients. Furthermore, this reduces the network load and the
latency of Blockchain transactions [1], [2].
II. RE LATE D WORK
To make data access policies accessible at each level re-
searchers proposed blockchain based systems integrated with
edge computing. By implementing AI at edge nodes further
decreased privacy vulnerabilities. Mamoshina. P et. al pro-
posed access policies to accelerate the private patients data
and implement deep learning algorithms to turns raw data
into strong useful information which can be used in bigger
perspectives [1]. In [3], Mackey et. al proposed blockchain
based data privacy control opportunities and challenges which
are significant enough in healthcare applications. A similar
approach was presented by Peterson et. al [4].
III. ETHEREUM BLOCKCHAIN WITH EDGE AI
To exclude intermediaries involve in data transactions in
edge devices, we define a platform in which the Blockchain
paradigm is extended into scarce computing devices. Ethereum
blockchain is used as a service platform to run smart contracts
to make the system autonomous in therms of its’ commu-
nication, processing and data dealing. A private ethereum
blockchain network is created by creating a genesis file. To
add every device, a pair of private and public key is generated
which will be used as a identifier of a device.
In our proposed system, all resource constrained sensor
nodes are directly connected to and rely on the edge gate-
ways which are often implemented by powerful single board
computers able to work as miner nodes to store, analyze and
aggregate raw data. Miner nodes can run neural networks
to process the raw data received from sensor nodes. With
a predefined time interval, edge nodes process the raw data,
and save this processed information into a new data block
by creating a unique hash. This data block consists of two
parts, header and body. The body apart contains processed
information and header part consists of general characteristics
about the processed information. This includes hash of previ-
ous block, time stamp, raw data definition and the type of data,
which can be further use for combining heterogeneous data at
bigger level for the sake of intelligent systems. To protect
the hash of data block, symmetric cryptography is used. After
encryption, the data blocks are saved on a blockchain cloud
and key is only hold by the end-device. Which will be later
used by a client to decrypt the desired data. Moreover, every
access to the data will be recorded. The proposed architecture
is illustrated in Figure 1, which is composed of four layers.
SENSORLAYER EDGELAYER-BLOCKCHAINNODES CLOUDLAYER END-USER
APPLICATION
Global Storage for Encrypted Data
Cloud Services
Web/Mobile Application Servers
Bio-signals analysis
ECG Feature Extraction and Storage in the Blockchain
Fig. 1. Proposed System Architecture
0 0.511.5 2
(a) Fragment of raw data over 2.3 seconds
(b) Extracted cycle template
2 4 6 8 10
60
80
100
(c) Heart rate over 10 seconds
Fig. 2. Results of the data analysis at the edge gateway.
ECG TCT RT TRD
150
72
36 35
Fig. 3. Execution time of the different processes (ms).
IV. EXP ER IM EN T AN D RES ULTS
The raw ECG data collected from a healthy 30 year-old
male person is shown in Figure 2. The data is sent to a smart
Edge-assisted gateway which extracts different ECG features
such as heart rate [5]. We have utilized a Raspberry Pi model
3 as the edge gateway, which in turn runs a node of the
Ethereum Blockchain. In order to test the feasibility of the
proposed model, we accumulate data for 10 seconds and then
analyze it. The data analysis requires around 150ms for the
feature extraction. Then, the results are encrypted and stored
in a distributed storage solution. The metadata is stored in
the blockchain. Figure 3 shows the execution time of the
analysis process (ECG), a data retrieval transaction (TRD),
a transaction confirmation (TCT) and the response time (RT).
In total, the system needs around 300ms to process one batch
of data, which runs every 10 seconds. Therefore, one gateway
could support up to 20 or 30 end-devices with the proposed
architecture.
V. CONCLUSION AND FUTURE WORK
Integrating Blockchain with Edge computing opens new
paradigms in privacy-critical and data-sensitive applications.
Our proposed architecture, combining a distributed ledger
with AI at the edge, creates secure database of processed
information which can only be used with the permission of its
owner. By Edge AI we refer to local decision making and data
processing at the edge computing layer. End devices can di-
rectly control all the processing, analyzing and sharing of their
data by updating their policies via ethereum smart contracts.
Implementing AI at edge nodes reduces resource consumption
like bandwidth required to upload data to blockchain cloud as
well as local storage. This data analysis step also increases
privacy by storing only processed information rather than raw
data.
In future work, we will analyze the scalability of the
proposed approach and alternative applications and experiment
with the integration of more complex deep learning algorithms.
We will study the utilization of Ethereum 2 for more scalable
systems.
REFERENCES
[1] P. Mamoshina et al. Converging blockchain and next-generation artifi-
cial intelligence technologies to decentralize and accelerate biomedical
research and healthcare. Oncotarget, 9(5):5665, 2018.
[2] J. Pe˜
na Queralta et al. Edge-AI in LoRa-based healthcare monitoring:
A case study on fall detection system with LSTM Recurrent Neural
Networks. In 42nd TSP, 2019.
[3] T. K. Mackey et al. ‘fit-for-purpose?’–challenges and opportunities for
applications of blockchain technology in the future of healthcare. BMC
medicine, 17(1):68, 2019.
[4] K. Peterson et al. A blockchain-based approach to health information
exchange networks. In NIST W. Blockchain Healthcare, 2016.
[5] C. Carreiras et al. BioSPPy: Biosignal processing in Python, 2015–.
[Online; accessed Aug. 2019].
... Amin and Hossain (2021) conducted a survey to evaluate the recent and revolutionising frameworks of edge computing, effective technologies for smart healthcare services, challenges and opportunities of different scenarios related to applications [18]. S. U. Amin and Hossain (2021) evaluated edge intelligence which is dedicated to targeting the classifications of health data with the identification and tracking of vital signs by utilising the deep learning technology where billions of edge devices are connected (see Figure 3) [18]. ...
... Amin and Hossain (2021) conducted a survey to evaluate the recent and revolutionising frameworks of edge computing, effective technologies for smart healthcare services, challenges and opportunities of different scenarios related to applications [18]. S. U. Amin and Hossain (2021) evaluated edge intelligence which is dedicated to targeting the classifications of health data with the identification and tracking of vital signs by utilising the deep learning technology where billions of edge devices are connected (see Figure 3) [18]. Another purpose of this study was to comprehensively analyse the usage of classification based on cutting edge AI and techniques that can be implemented for edge intelligence. ...
... Another purpose of this study was to comprehensively analyse the usage of classification based on cutting edge AI and techniques that can be implemented for edge intelligence. However, it cannot be neglected that challenges related to security and complex computation are associated with Edge AI [18]. e contribution of this study is that it has provided potential recommendations based on research for enhancing the services related to Edge AI computation for healthcare services in smart cities. ...
Article
Full-text available
Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.
... Presenting a data trading platform using Ethereum with consensus of proof-of-concept (PoC). No [159] Data Management Security and privacy-assured data management framework for privacy-critical systems. ...
Preprint
As an important technology to ensure data security, consistency, traceability, etc., blockchain has been increasingly used in Internet of Things (IoT) applications. The integration of blockchain and edge computing can further improve the resource utilization in terms of network, computing, storage, and security. This paper aims to present a survey on the integration of blockchain and edge computing. In particular, we first give an overview of blockchain and edge computing. We then present a general architecture of an integration of blockchain and edge computing system. We next study how to utilize blockchain to benefit edge computing, as well as how to use edge computing to benefit blockchain. We also discuss the issues brought by the integration of blockchain and edge computing system and solutions from perspectives of resource management, joint optimization, data management, computation offloading and security mechanism. Finally, we analyze and summarize the existing challenges posed by the integration of blockchain and edge computing system and the potential solutions in the future.
... A concrete implementation considers arrhythmia detection with a CNN in the edge, storing the resulting output along with device ID and other transactional metadata in an Ethereum chain. Similarly, blockchain applications can aid with privacy concerning sensitive data in the edge by processing data locally using AI, and keeping track of all parties accessing the resulting features by using an Ethereum chain [130]. A different application combines blockchain-based smart contracts with trustless smart oracles for trust management in the fog computing platform of DECENTER [131]. ...
... AntiConcealer is an Edge AI approach for detecting adversary concealed behaviors in the IoT [18]. For the security solutions, Edge AI is also used for anomaly detection in the advanced metering infrastructures [19], while Nawaz et al. introduce Ethereum blockchain based solution for analysing the data and tracking the parties accessing that analysis data [20]. ...
Conference Paper
The modern trend of moving artificial intelligence computation near to the origin of data sources has increased the demand for new hardware and software suitable for such environments. We carried out a scoping study to find the current resources used when developing Edge AI applications. Due to the nature of the topic, the research combined scientific sources with product information and software project sources. The paper is structured as follows. In the first part, Edge AI applications are briefly discussed followed by hardware options and finally, the software used to develop AI models is described. There are various hardware products available, and we found as many as possible for this research to identify the best-known manufacturers. We describe the devices in the following categories: artificial intelligence accelerators and processors, field-programmable gate arrays, system-on-a-chip devices, system-on-modules, and full computers from development boards to servers. There seem to be three trends in Edge AI software development: neural network optimization, mobile device software and microcontroller software. We discussed these emerging fields and how the special challenges of low power consumption and machine learning computation are being taken into account. Our findings suggest that the Edge AI ecosystem is currently developing, and it has its own challenges to which vendors and developers are responding.
... Some interesting open research issues are designing a blockchain-based trust model for the FEC environment and developing distributed security frameworks using hybrid SDN blockchain [149,150] to provide continuous monitoring without a single point failure. Additionally, developing authentication schemes for FEC devices, presenting trusted data management models to support privacy and authorization [151][152][153], combining artificial intelligence and blockchain to improve FEC security [154,155], and developing robust and lightweight optimization algorithms for the blockchain ecosystem are the other future research trends. The use of blockchain in the FEC trust management has not been investigated completely and remains an open issue. ...
Article
Cloud computing provides software, infrastructure, and platform as services and reduces the cost of usage for cloud customers. Recently, a system architecture called Fog and Edge Computing (FEC) has been introduced that fills the gap between cloud and things toward the continuum of service and optimizes cloud computing resources by processing time-sensitive data near the data generation source at the network edge. Since the FEC environment includes myriad heterogeneous computing nodes, some of the FEC nodes may be un-trustful or even malicious; therefore, these un-trustworthy nodes could disrupt the normal activity of FEC in data storing and processing. Consequently, FEC trust management is crucial to provide trustworthy data processing and improve user privacy. Despite the critical importance of trust management issues in the FEC, any systematic review in this field has not been performed. This paper presents a systematic review of 74 high-quality articles related to FEC trust management published between 2015 and July 2021. To this end, selected FEC trust management approaches are categorized into three main classes: algorithm, architecture, and model/framework. Additionally, this paper discusses and compares the FEC trust management approaches based on merits and demerits, evaluation techniques, tools and simulation environments, and important trust metrics. Finally, some open issues and future trends for the oncoming studies are highlighted.
... It achieves this by using fuzzy hashing to detect suspicious activities, such as poisoning attacks in FL-trained models. The authors of [17] also proposed an architecture for data analysis at the edge based on blockchain and AI. The aim is to enhance the security of privacy-critical systems, such as healthcare applications, by restricting raw data to producers only. ...
Article
Full-text available
In this study, a new blockchain protocol and a novel architecture that integrate the advantages offered by edge computing, artificial intelligence (AI), IoT end-devices, and blockchain were designed, developed, and validated. This new architecture has the ability to monitor the environment, collect data, analyze it, process it using an AI-expert engine, provide predictions and actionable outcomes, and finally share it on a public blockchain platform. For the use-case implementation, the pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used to test and evaluate the proposed system. Recently, various authors traced the spread of viruses in sewage water and studied how it can be used as a tracking system. Early warning notifications can allow governments and organizations to take appropriate actions at the earliest stages possible. The system was validated experimentally using 14 Raspberry Pis, and the results and analyses proved that the system is able to utilize low-cost and low-power flexible IoT hardware at the processing layer to detect COVID-19 and predict its spread using the AI engine, with an accuracy of 95%, and share the outcome over the blockchain platform. This is accomplished when the platform is secured by the honesty-based distributed proof of authority (HDPoA) and without any substantial impact on the devices’ power sources, as there was only a power consumption increase of 7% when the Raspberry Pi was used for blockchain mining and 14% when used to produce an AI prediction.
... The potential of blockchain technology to handle problems like double spend, consent, data integrity, transactional trust, identity, and overall information security appears to be driving its revolution and evolution [19]. The substance of what is recorded on the ledger, the procedure used to obtain consensus, and the degree to which the ledger is permissioned will all influence the design of such blockchain applications [20]. ...
Article
Full-text available
Communication between different organisations and within different components of the organization itself is one of the trending issues nowadays. Over the past year, it has proven to be essential for many businesses left short on staff due to COVID-19, and communications have shifted from face-to-face to calls and emails. In the corporate world, communication is essential, and information technology provides our firm with the tools it needs to communicate quickly and effectively. Information technology can benefit our company by allowing for faster communication, electronic storage, and record protection. Organizations, whether they are educational institutes or any commercial organization, contain sensitive data which is meant to be kept confidential and integrated, depending upon the nature of the data. Different organisations may include several components, i.e., educational institutions such as universities consist of several faculties or departments and administrative departments, while commercial organisations consist of many departments. The administrative department can be far away from the other departments, though the letter issued can be changed by any harmful person, and data that is incorporated into the letter can be exploited and attacked. Keeping these problems in mind, this research was planned to overcome the above issues of security by using trending technologies, including blockchain, by designing a secure model that can be used to solve the confidentiality issues and integrity issues of communication between different organisations and inter-organizational communication to exchange information and data between and in-between them. In the proposed model, departments and distributed servers will be connected with each other with the help of an interface. In the departments, the administrators of personals will be authenticated and authorized by credentials and biometrics to keep the data secure. To communicate, a read/write request will be sent to the server, and the output of that request will be shown on the department side. To keep the data secure and untemper able, it was stored in a blockchain structure. Each block contained the data and was connected with other blocks. The proposed model was also validated by calculating empirical results in which a sample application was developed and tested three times at Sindh Agriculture University Tandojam while varying the different scenarios. The results proved the proposed model to be secure and easy to use. However, it also increased the confidentiality and integrity of the data.
... The potential of blockchain technology to handle problems like double spend, consent, data integrity, transactional trust, identity, and overall information security appears to be driving its revolution and evolution [19]. The substance of what is recorded on the ledger, the procedure used to obtain consensus, and the degree to which the ledger is permissioned will all influence the design of such blockchain applications [20]. ...
Article
Full-text available
Communication between different organisations and within different components of the organization itself is one of the trending issues nowadays. Over the past year, it has proven to be essential for many businesses left short on staff due to COVID-19, and communications have shifted from face-to-face to calls and emails. In the corporate world, communication is essential, and information technology provides our firm with the tools it needs to communicate quickly and effectively. Information technology can benefit our company by allowing for faster communication, electronic storage, and record protection. Organizations, whether they are educational institutes or any commercial organization, contain sensitive data which is meant to be kept confidential and integrated, depending upon the nature of the data. Different organisations may include several components, i.e., educational institutions such as universities consist of several faculties or departments and administrative departments, while commercial organisations consist of many departments. The administrative department can be far away from the other departments, though the letter issued can be changed by any harmful person, and data that is incorporated into the letter can be exploited and attacked. Keeping these problems in mind, this research was planned to overcome the above issues of security by using trending technologies, including blockchain, by designing a secure model that can be used to solve the confidentiality issues and integrity issues of communication between different organisations and inter-organizational communication to exchange information and data between and in-between them. In the proposed model, departments and distributed servers will be connected with each other with the help of an interface. In the departments, the administrators of personals will be authenticated and authorized by credentials and biometrics to keep the data secure. To communicate, a read/write request will be sent to the server, and the output of that request will be shown on the department side. To keep the data secure and untemper able, it was stored in a blockchain structure. Each block contained the data and was connected with other blocks. The proposed model was also validated by calculating empirical results in which a sample application was developed and tested three times at Sindh Agriculture University Tandojam while varying the different scenarios. The results proved the proposed model to be secure and easy to use. However, it also increased the confidentiality and integrity of the data.
... Privacy preservation is the prominent issue in the mission-critical application of BEoT standard. The study in [136] have suggested the integration of AI at edge nodes aiming at the higher level of the privacy to the users with fewer security vulnerabilities by alleviating the use of third parties to mine the data at the edge. The proposed model suggests the storage of processed data in blocks instead of storing the raw data and retrieving it back for processing later. ...
Preprint
Full-text available
In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, called BEoT that has been regarded as a promising enabler for future services and applications. In this paper, we present a state-of-the-art review of recent developments in BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home and smart grid. Security challenges in BEoT paradigm are also discussed and analyzed, with some key services such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.
Chapter
Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in an inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives.
Conference Paper
Full-text available
Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90\% and an average recall over 95\% in fall detection.
Article
Full-text available
Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin – the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as ‘revolutionary’ and ‘disruptive’, is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is ‘fit-for-purpose’ is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.
Article
Full-text available
The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.
BioSPPy: Biosignal processing in Python
  • C Carreiras
A blockchain-based approach to health information exchange networks
  • K Peterson
K. Peterson et al. A blockchain-based approach to health information exchange networks. In NIST W. Blockchain Healthcare, 2016.