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2021-07-07
Aski, Vidyadhar, Dhaka, Vijaypal Singh, Kumar, Sunil, Parashar, Anubha, Ladagi, Akshata.  2020.  A Multi-Factor Access Control and Ownership Transfer Framework for Future Generation Healthcare Systems. 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). :93–98.
The recent advancements in ubiquitous sensing powered by Wireless Computing Technologies (WCT) and Cloud Computing Services (CCS) have introduced a new thinking ability amongst researchers and healthcare professionals for building secure and connected healthcare systems. The integration of Internet of Things (IoT) in healthcare services further brings in several challenges with it, mainly including encrypted communication through vulnerable wireless medium, authentication and access control algorithms and ownership transfer schemes (important patient information). Major concern of such giant connected systems lies in creating the data handling strategies which is collected from the billions of heterogeneous devices distributed across the hospital network. Besides, the resource constrained nature of IoT would make these goals difficult to achieve. Motivated by aforementioned deliberations, this paper introduces a novel approach in designing a security framework for edge-computing based connected healthcare systems. An efficient, multi-factor access control and ownership transfer mechanism for edge-computing based futuristic healthcare applications is the core of proposed framework. Data scalability is achieved by employing distributed approach for clustering techniques that analyze and aggregate voluminous data acquired from heterogeneous devices individually before it transits the to the cloud. Moreover, data/device ownership transfer scheme is considered to be the first time in its kind. During ownership transfer phase, medical server facilitates user to transfer the patient information/ device ownership rights to the other registered users. In order to avoid the existing mistakes, we propose a formal and informal security analysis, that ensures the resistance towards most common IoT attacks such as insider attack, denial of distributed service (DDoS) attack and traceability attacks.
2021-07-02
Braeken, An, Porambage, Pawani, Puvaneswaran, Amirthan, Liyanage, Madhusanka.  2020.  ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare. 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). :1—7.
The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.
2021-06-28
Miatra, Ayati, Kumar, Sumit.  2020.  Security Issues With Fog Computing. 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence). :123–128.
Fog computing or edge computing or fogging extends cloud computing to the edge of the network. It operates on the computing, storage and networking services between user-end devices and cloud computing data centres. However, in the process of caring out these operations, fog computing is faced with several security issues. These issues may be inherited from cloud computing systems or may arise due to fog computing systems alone. Some of the major gaps in providing a secure platform for the fog computing process arise from interim operational steps like authentication or identification, which often expands to large scale performance issues in fog computing. Thus, these issues and their implications on fog computing databases, and the possible available solutions are researched and provided for a better scope of future use and growth of fog computing systems by bridging the gaps of security issues in it.
2021-05-13
Xu, Shawn, Venugopalan, Subhashini, Sundararajan, Mukund.  2020.  Attribution in Scale and Space. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :9677–9686.
We study the attribution problem for deep networks applied to perception tasks. For vision tasks, attribution techniques attribute the prediction of a network to the pixels of the input image. We propose a new technique called Blur Integrated Gradients (Blur IG). This technique has several advantages over other methods. First, it can tell at what scale a network recognizes an object. It produces scores in the scale/frequency dimension, that we find captures interesting phenomena. Second, it satisfies the scale-space axioms, which imply that it employs perturbations that are free of artifact. We therefore produce explanations that are cleaner and consistent with the operation of deep networks. Third, it eliminates the need for baseline parameter for Integrated Gradients for perception tasks. This is desirable because the choice of baseline has a significant effect on the explanations. We compare the proposed technique against previous techniques and demonstrate application on three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and AudioSet audio event identification. Code and examples are at https://github.com/PAIR-code/saliency.
2021-04-27
Javid, T., Faris, M., Beenish, H., Fahad, M..  2020.  Cybersecurity and Data Privacy in the Cloudlet for Preliminary Healthcare Big Data Analytics. 2020 International Conference on Computing and Information Technology (ICCIT-1441). :1—4.

In cyber physical systems, cybersecurity and data privacy are among most critical considerations when dealing with communications, processing, and storage of data. Geospatial data and medical data are examples of big data that require seamless integration with computational algorithms as outlined in Industry 4.0 towards adoption of fourth industrial revolution. Healthcare Industry 4.0 is an application of the design principles of Industry 4.0 to the medical domain. Mobile applications are now widely used to accomplish important business functions in almost all industries. These mobile devices, however, are resource poor and proved insufficient for many important medical applications. Resource rich cloud services are used to augment poor mobile device resources for data and compute intensive applications in the mobile cloud computing paradigm. However, the performance of cloud services is undesirable for data-intensive, latency-sensitive mobile applications due increased hop count between the mobile device and the cloud server. Cloudlets are virtual machines hosted in server placed nearby the mobile device and offer an attractive alternative to the mobile cloud computing in the form of mobile edge computing. This paper outlines cybersecurity and data privacy aspects for communications of measured patient data from wearable wireless biosensors to nearby cloudlet host server in order to facilitate the cloudlet based preliminary and essential complex analytics for the medical big data.

2021-03-29
Juyal, S., Sharma, S., Harbola, A., Shukla, A. S..  2020.  Privacy and Security of IoT based Skin Monitoring System using Blockchain Approach. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). :1—5.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

2021-03-09
Suresh, V., Rajashree, S..  2020.  Establishing Authenticity for DICOM images using ECC algorithm. 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII). :1—4.

Preserving medical data is of utmost importance to stake holders. There are not many laws in India about preservation, usability of patient records. When data is transmitted across the globe there are chances of data getting tampered intentionally or accidentally. Tampered data loses its authenticity for diagnostic purpose, research and various other reasons. This paper proposes an authenticity based ECDSA algorithm by signature verification to identify the tampering of medical image files and alerts by the rules of authenticity. The algorithm can be used by researchers, doctors or any other educated person in order to maintain the authenticity of the record. Presently it is applied on medical related image files like DICOM. However, it can support any other medical related image files and still preserve the authenticity.

2021-02-23
Liu, W., Park, E. K., Krieger, U., Zhu, S. S..  2020.  Smart e-Health Security and Safety Monitoring with Machine Learning Services. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1—6.

This research provides security and safety extensions to a blockchain based solution whose target is e-health. The Advanced Blockchain platform is extended with intelligent monitoring for security and machine learning for detecting patient treatment medication safety issues. For the reasons of stringent HIPAA, HITECH, EU-GDPR and other regional regulations dictating security, safety and privacy requirements, the e-Health blockchains have to cover mandatory disclosure of violations or enforcements of policies during transaction flows involving healthcare. Our service solution further provides the benefits of resolving the abnormal flows of a medical treatment process, providing accountability of the service providers, enabling a trust health information environment for institutions to handle medication safely, giving patients a better safety guarantee, and enabling the authorities to supervise the security and safety of e-Health blockchains. The capabilities can be generalized to support a uniform smart solution across industry in a variety of blockchain applications.

2021-02-22
Martinelli, F., Marulli, F., Mercaldo, F., Marrone, S., Santone, A..  2020.  Enhanced Privacy and Data Protection using Natural Language Processing and Artificial Intelligence. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.

Artificial Intelligence systems have enabled significant benefits for users and society, but whilst the data for their feeding are always increasing, a side to privacy and security leaks is offered. The severe vulnerabilities to the right to privacy obliged governments to enact specific regulations to ensure privacy preservation in any kind of transaction involving sensitive information. In the case of digital and/or physical documents comprising sensitive information, the right to privacy can be preserved by data obfuscation procedures. The capability of recognizing sensitive information for obfuscation is typically entrusted to the experience of human experts, who are over-whelmed by the ever increasing amount of documents to process. Artificial intelligence could proficiently mitigate the effort of the human officers and speed up processes. Anyway, until enough knowledge won't be available in a machine readable format, automatic and effectively working systems can't be developed. In this work we propose a methodology for transferring and leveraging general knowledge across specific-domain tasks. We built, from scratch, specific-domain knowledge data sets, for training artificial intelligence models supporting human experts in privacy preserving tasks. We exploited a mixture of natural language processing techniques applied to unlabeled domain-specific documents corpora for automatically obtain labeled documents, where sensitive information are recognized and tagged. We performed preliminary tests just over 10.000 documents from the healthcare and justice domains. Human experts supported us during the validation. Results we obtained, estimated in terms of precision, recall and F1-score metrics across these two domains, were promising and encouraged us to further investigations.

2021-02-03
Kaneriya, J., Patel, H..  2020.  A Comparative Survey on Blockchain Based Self Sovereign Identity System. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). :1150—1155.

The Internet has changed business, education, healthcare, banking etc. and it is the main part of technological evolution. Internet provides us a connecting world to perform our day to day life activities easily. Internet is designed in such a way that it can uniquely identify machine, not a person, on the network hence there is need to design a system that can perform entity identification on the Internet. Currently on Internet, service providers provide identity of a user with user name and password and store this information on a centralized server. These servers become honey pot for hackers to steal user’s personal identity information and service provider can utilize user identity information using data mining, artificial intelligence for economic benefits. Aim of Self sovereign identity system is to provide decentralized, user centric identity system which is controlled by identity owner that can be developed along with distributed ledger technology i.e. blockchain. In this paper, we intend to make an exhaustive study on different blockchain based self sovereign identity implementations (such as Sovrin, Uport, EverID, LifeID, Sora, SelfKey) along with its architectural components and discuss about use case of self sovereign identity.

2021-01-28
Sammoud, A., Chalouf, M. A., Hamdi, O., Montavont, N., Bouallegue, A..  2020.  A secure three-factor authentication and biometrics-based key agreement scheme for TMIS with user anonymity. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1916—1921.

E- Health systems, specifically, Telecare Medical Information Systems (TMIS), are deployed in order to provide patients with specific diseases with healthcare services that are usually based on remote monitoring. Therefore, making an efficient, convenient and secure connection between users and medical servers over insecure channels within medical services is a rather major issue. In this context, because of the biometrics' characteristics, many biometrics-based three factor user authentication schemes have been proposed in the literature to secure user/server communication within medical services. In this paper, we make a brief study of the most interesting proposals. Then, we propose a new three-factor authentication and key agreement scheme for TMIS. Our scheme tends not only to fix the security drawbacks of some studied related work, but also, offers additional significant features while minimizing resource consumption. In addition, we perform a formal verification using the widely accepted formal security verification tool AVISPA to demonstrate that our proposed scheme is secure. Also, our comparative performance analysis reveals that our proposed scheme provides a lower resource consumption compared to other related work's proposals.

2021-01-15
Katarya, R., Lal, A..  2020.  A Study on Combating Emerging Threat of Deepfake Weaponization. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :485—490.
A breakthrough in the emerging use of machine learning and deep learning is the concept of autoencoders and GAN (Generative Adversarial Networks), architectures that can generate believable synthetic content called deepfakes. The threat lies when these low-tech doctored images, videos, and audios blur the line between fake and genuine content and are used as weapons to cause damage to an unprecedented degree. This paper presents a survey of the underlying technology of deepfakes and methods proposed for their detection. Based on a detailed study of all the proposed models of detection, this paper presents SSTNet as the best model to date, that uses spatial, temporal, and steganalysis for detection. The threat posed by document and signature forgery, which is yet to be explored by researchers, has also been highlighted in this paper. This paper concludes with the discussion of research directions in this field and the development of more robust techniques to deal with the increasing threats surrounding deepfake technology.
2020-12-01
Poulsen, A., Burmeister, O. K., Tien, D..  2018.  Care Robot Transparency Isn't Enough for Trust. 2018 IEEE Region Ten Symposium (Tensymp). :293—297.

A recent study featuring a new kind of care robot indicated that participants expect a robot's ethical decision-making to be transparent to develop trust, even though the same type of `inspection of thoughts' isn't expected of a human carer. At first glance, this might suggest that robot transparency mechanisms are required for users to develop trust in robot-made ethical decisions. But the participants were found to desire transparency only when they didn't know the specifics of a human-robot social interaction. Humans trust others without observing their thoughts, which implies other means of determining trustworthiness. The study reported here suggests that the method is social interaction and observation, signifying that trust is a social construct. Moreover, that `social determinants of trust' are the transparent elements. This socially determined behaviour draws on notions of virtue ethics. If a caregiver (nurse or robot) consistently provides good, ethical care, then patients can trust that caregiver to do so often. The same social determinants may apply to care robots and thus it ought to be possible to trust them without the ability to see their thoughts. This study suggests why transparency mechanisms may not be effective in helping to develop trust in care robot ethical decision-making. It suggests that roboticists need to build sociable elements into care robots to help patients to develop patient trust in the care robot's ethical decision-making.

Xu, J., Howard, A..  2018.  The Impact of First Impressions on Human- Robot Trust During Problem-Solving Scenarios. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). :435—441.

With recent advances in robotics, it is expected that robots will become increasingly common in human environments, such as in the home and workplaces. Robots will assist and collaborate with humans on a variety of tasks. During these collaborations, it is inevitable that disagreements in decisions would occur between humans and robots. Among factors that lead to which decision a human should ultimately follow, theirs or the robot, trust is a critical factor to consider. This study aims to investigate individuals' behaviors and aspects of trust in a problem-solving situation in which a decision must be made in a bounded amount of time. A between-subject experiment was conducted with 100 participants. With the assistance of a humanoid robot, participants were requested to tackle a cognitive-based task within a given time frame. Each participant was randomly assigned to one of the following initial conditions: 1) a working robot in which the robot provided a correct answer or 2) a faulty robot in which the robot provided an incorrect answer. Impacts of the faulty robot behavior on participant's decision to follow the robot's suggested answer were analyzed. Survey responses about trust were collected after interacting with the robot. Results indicated that the first impression has a significant impact on participant's behavior of trusting a robot's advice during a disagreement. In addition, this study discovered evidence supporting that individuals still have trust in a malfunctioning robot even after they have observed a robot's faulty behavior.

2020-11-09
Farhadi, M., Haddad, H., Shahriar, H..  2019.  Compliance Checking of Open Source EHR Applications for HIPAA and ONC Security and Privacy Requirements. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:704–713.
Electronic Health Record (EHR) applications are digital versions of paper-based patient's health information. They are increasingly adopted to improved quality in healthcare, such as convenient access to histories of patient medication and clinic visits, easier follow up of patient treatment plans, and precise medical decision-making process. EHR applications are guided by measures of the Health Insurance Portability and Accountability Act (HIPAA) to ensure confidentiality, integrity, and availability. Furthermore, Office of the National Coordinator (ONC) for Health Information Technology (HIT) certification criteria for usability of EHRs. A compliance checking approach attempts to identify whether or not an adopted EHR application meets the security and privacy criteria. There is no study in the literature to understand whether traditional static code analysis-based vulnerability discovered can assist in compliance checking of regulatory requirements of HIPAA and ONC. This paper attempts to address this issue. We identify security and privacy requirements for HIPAA technical requirements, and identify a subset of ONC criteria related to security and privacy, and then evaluate EHR applications for security vulnerabilities. Finally propose mitigation of security issues towards better compliance and to help practitioners reuse open source tools towards certification compliance.
2020-10-16
Tungela, Nomawethu, Mutudi, Maria, Iyamu, Tiko.  2018.  The Roles of E-Government in Healthcare from the Perspective of Structuration Theory. 2018 Open Innovations Conference (OI). :332—338.

The e-government concept and healthcare have usually been studied separately. Even when and where both e-government and healthcare systems were combined in a study, the roles of e-government in healthcare have not been examined. As a result., the complementarity of the systems poses potential challenges. The interpretive approach was applied in this study. Existing materials in the areas of healthcare and e-government were used as data from a qualitative method viewpoint. Dimension of change from the perspective of the structuration theory was employed to guide the data analysis. From the analysis., six factors were found to be the main roles of e-government in the implementation and application of e-health in the delivering of healthcare services. An understanding of the roles of e-government promotes complementarity., which enhances the healthcare service delivery to the community.

2020-10-12
Khayat, Mohamad, Barka, Ezedin, Sallabi, Farag.  2019.  SDN\_Based Secure Healthcare Monitoring System(SDN-SHMS). 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1–7.
Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.
2020-10-06
Ibrahim, Romani Farid.  2019.  Mobile Transaction Processing for a Distributed War Environment. 2019 14th International Conference on Computer Science Education (ICCSE). :856—862.

The battlefield environment differs from the natural environment in terms of irregular communications and the possibility of destroying communication and medical units by enemy forces. Information that can be collected in a war environment by soldiers is important information and must reach top-level commanders in time for timely decisions making. Also, ambulance staff in the battlefield need to enter the data of injured soldiers after the first aid, so that the information is available for the field hospital staff to prepare the needs for incoming injured soldiers.In this research, we propose two transaction techniques to handle these issues and use different concurrency control protocols, depending on the nature of the transaction and not a one concurrency control protocol for all types of transactions. Message transaction technique is used to collect valuable data from the battlefield by soldiers and allows top-level commanders to view it according to their permissions by logging into the system, to help them make timely decisions. In addition, use the capabilities of DBMS tools to organize data and generate reports, as well as for future analysis. Medical service unit transactional workflow technique is used to provides medical information to the medical authorities about the injured soldiers and their status, which helps them to prepare the required needs before the wounded soldiers arrive at the hospitals. Both techniques handle the disconnection problem during transaction processing.In our approach, the transaction consists of four phases, reading, editing, validation, and writing phases, and its processing is based on the optimistic concurrency control protocol, and the rules of actionability that describe how a transaction behaves if a value-change is occurred on one or more of its attributes during its processing time by other transactions.

2020-09-28
Abie, Habtamu.  2019.  Cognitive Cybersecurity for CPS-IoT Enabled Healthcare Ecosystems. 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT). :1–6.

Cyber Physical Systems (CPS)-Internet of Things (IoT) enabled healthcare services and infrastructures improve human life, but are vulnerable to a variety of emerging cyber-attacks. Cybersecurity specialists are finding it hard to keep pace of the increasingly sophisticated attack methods. There is a critical need for innovative cognitive cybersecurity for CPS-IoT enabled healthcare ecosystem. This paper presents a cognitive cybersecurity framework for simulating the human cognitive behaviour to anticipate and respond to new and emerging cybersecurity and privacy threats to CPS-IoT and critical infrastructure systems. It includes the conceptualisation and description of a layered architecture which combines Artificial Intelligence, cognitive methods and innovative security mechanisms.

Fimiani, Gianluca.  2018.  Supporting Privacy in a Cloud-Based Health Information System by Means of Fuzzy Conditional Identity-Based Proxy Re-encryption (FCI-PRE). 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA). :569–572.
Healthcare is traditionally a data-intensive domain, where physicians needs complete and updated anamnesis of their patients to take the best medical decisions. Dematerialization of the medical documents and the consequent health information systems to share electronic health records among healthcare providers are paving the way to an effective solution to this issue. However, they are also paving the way of non-negligible privacy issues that are limiting the full application of these technologies. Encryption is a valuable means to resolve such issues, however the current schemes are not able to cope with all the needs and challenges that the cloud-based sharing of electronic health records imposes. In this work we have investigated the use of a novel scheme where encryption is combined with biometric authentication, and defines a preliminary solution.
Guo, Hao, Li, Wanxin, Nejad, Mark, Shen, Chien-Chung.  2019.  Access Control for Electronic Health Records with Hybrid Blockchain-Edge Architecture. 2019 IEEE International Conference on Blockchain (Blockchain). :44–51.
The global Electronic Health Record (EHR) market is growing dramatically and expected to reach \$39.7 billions by 2022. To safe-guard security and privacy of EHR, access control is an essential mechanism for managing EHR data. This paper proposes a hybrid architecture to facilitate access control of EHR data by using both blockchain and edge node. Within the architecture, a blockchain-based controller manages identity and access control policies and serves as a tamper-proof log of access events. In addition, off-chain edge nodes store the EHR data and apply policies specified in Abbreviated Language For Authorization (ALFA) to enforce attribute-based access control on EHR data in collaboration with the blockchain-based access control logs. We evaluate the proposed hybrid architecture by utilizing Hyperledger Composer Fabric blockchain to measure the performance of executing smart contracts and ACL policies in terms of transaction processing time and response time against unauthorized data retrieval.
2020-09-21
Xin, Yang, Qian, Zhenwei, Jiang, Rong, Song, Yang.  2019.  Trust Evaluation Strategy Based on Grey System Theory for Medical Big Data. 2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI). :157–160.
The performance of the trust evaluation strategy depends on the accuracy and rationality of the trust evaluation weight system. Trust is a difficult to accurate measurement and quantitative cognition in the heart, the trust of the traditional evaluation method has a strong subjectivity and fuzziness and uncertainty. This paper uses the AHP method to determine the trust evaluation index weight, and combined with grey system theory to build trust gray evaluation model. The use of gray assessment based on the whitening weight function in the evaluation process reduces the impact of the problem that the evaluation result of the trust evaluation is not easy to accurately quantify when the decision fuzzy and the operating mechanism are uncertain.
2020-08-28
Yau, Yiu Chung, Khethavath, Praveen, Figueroa, Jose A..  2019.  Secure Pattern-Based Data Sensitivity Framework for Big Data in Healthcare. 2019 IEEE International Conference on Big Data, Cloud Computing, Data Science Engineering (BCD). :65—70.
With the exponential growth in the usage of electronic medical records (EMR), the amount of data generated by the healthcare industry has too increased exponentially. These large amounts of data, known as “Big Data” is mostly unstructured. Special big data analytics methods are required to process the information and retrieve information which is meaningful. As patient information in hospitals and other healthcare facilities become increasingly electronic, Big Data technologies are needed now more than ever to manage and understand this data. In addition, this information tends to be quite sensitive and needs a highly secure environment. However, current security algorithms are hard to be implemented because it would take a huge amount of time and resources. Security protocols in Big data are also not adequate in protecting sensitive information in the healthcare. As a result, the healthcare data is both heterogeneous and insecure. As a solution we propose the Secure Pattern-Based Data Sensitivity Framework (PBDSF), that uses machine learning mechanisms to identify the common set of attributes of patient data, data frequency, various patterns of codes used to identify specific conditions to secure sensitive information. The framework uses Hadoop and is built on Hadoop Distributed File System (HDFS) as a basis for our clusters of machines to process Big Data, and perform tasks such as identifying sensitive information in a huge amount of data and encrypting data that are identified to be sensitive.
2020-08-24
Harris, Daniel R., Delcher, Chris.  2019.  bench4gis: Benchmarking Privacy-aware Geocoding with Open Big Data. 2019 IEEE International Conference on Big Data (Big Data). :4067–4070.
Geocoding, the process of translating addresses to geographic coordinates, is a relatively straight-forward and well-studied process, but limitations due to privacy concerns may restrict usage of geographic data. The impact of these limitations are further compounded by the scale of the data, and in turn, also limits viable geocoding strategies. For example, healthcare data is protected by patient privacy laws in addition to possible institutional regulations that restrict external transmission and sharing of data. This results in the implementation of “in-house” geocoding solutions where data is processed behind an organization's firewall; quality assurance for these implementations is problematic because sensitive data cannot be used to externally validate results. In this paper, we present our software framework called bench4gis which benchmarks privacy-aware geocoding solutions by leveraging open big data as surrogate data for quality assurance; the scale of open big data sets for address data can ensure that results are geographically meaningful for the locale of the implementing institution.
2020-08-13
Kim, MyeongHyun, Lee, JoonYoung, Yu, SungJin, Park, KiSung, Park, YoHan, Park, YoungHo.  2019.  A Secure Authentication and Key Establishment Scheme for Wearable Devices. 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1—2.
With the rapid development of micro-electronics and Information and Communication Technology (ICT), users can utilize various service such as Internet of Things(IoT), smart-healthcare and smart-home using wearable devices. However, the sensitive information of user are revealed by attackers because the medical services are provided through open channel. Therefore, secure mutual authentication and key establishment are essential to provide secure services for legitimate users in Wireless Body Area Networks(WBAN). In 2019, Gupta et al. proposed a lightweight anonymous user authentication and key establishment scheme for wearable devices. We demonstrate that their scheme cannot withstand user impersonation, session key disclosure and wearable device stolen attacks. We also propose a secure and lightweight mutual authentication and key establishment scheme using wearable devices to resolve the security shortcomings of Gupta et al.'s scheme. The proposed scheme can be suitable to resource-limited environments.