7,081 research outputs found
Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier
As universities recognize the inherent value in the data they collect and
hold, they encounter unforeseen challenges in stewarding those data in ways
that balance accountability, transparency, and protection of privacy, academic
freedom, and intellectual property. Two parallel developments in academic data
collection are converging: (1) open access requirements, whereby researchers
must provide access to their data as a condition of obtaining grant funding or
publishing results in journals; and (2) the vast accumulation of 'grey data'
about individuals in their daily activities of research, teaching, learning,
services, and administration. The boundaries between research and grey data are
blurring, making it more difficult to assess the risks and responsibilities
associated with any data collection. Many sets of data, both research and grey,
fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities
are exploiting these data for research, learning analytics, faculty evaluation,
strategic decisions, and other sensitive matters. Commercial entities are
besieging universities with requests for access to data or for partnerships to
mine them. The privacy frontier facing research universities spans open access
practices, uses and misuses of data, public records requests, cyber risk, and
curating data for privacy protection. This paper explores the competing values
inherent in data stewardship and makes recommendations for practice, drawing on
the pioneering work of the University of California in privacy and information
security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201
Towards Security and Privacy in Networked Medical Devices and Electronic Healthcare Systems
E-health is a growing eld which utilizes wireless sensor networks to enable access to effective and efficient healthcare services and provide patient monitoring to enable early detection and treatment of health conditions. Due to the proliferation of e-health systems, security and privacy have become critical issues in preventing data falsification, unauthorized access to the system, or eavesdropping on sensitive health data. Furthermore, due to the intrinsic limitations of many wireless medical devices, including low power and limited computational resources, security and device performance can be difficult to balance. Therefore, many current networked medical devices operate without basic security services such as authentication, authorization, and encryption.
In this work, we survey recent work on e-health security, including biometric approaches, proximity-based approaches, key management techniques, audit mechanisms, anomaly detection, external device methods, and lightweight encryption and key management protocols. We also survey the state-of-the art in e-health privacy, including techniques such as obfuscation, secret sharing, distributed data mining, authentication, access control, blockchain, anonymization, and cryptography. We then propose a comprehensive system model for e-health applications with consideration of battery capacity and computational ability of medical devices. A case study is presented to show that the proposed system model can support heterogeneous medical devices with varying power and resource constraints. The case study demonstrates that it is possible to signicantly reduce the overhead for security on power-constrained devices based on the proposed system model
Security for networked smart healthcare systems: A systematic review
Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry
The RFID PIA – developed by industry, agreed by regulators
This chapter discusses the privacy impact assessment (PIA) framework endorsed
by the European Commission on February 11th, 2011. This PIA, the first to receive the
Commission's endorsement, was developed to deal with privacy challenges associated with
the deployment of radio frequency identification (RFID) technology, a key building block of
the Internet of Things. The goal of this chapter is to present the methodology and key
constructs of the RFID PIA Framework in more detail than was possible in the official text.
RFID operators can use this article as a support document when they conduct PIAs and need
to interpret the PIA Framework. The chapter begins with a history of why and how the PIA
Framework for RFID came about. It then proceeds with a description of the endorsed PIA
process for RFID applications and explains in detail how this process is supposed to function.
It provides examples discussed during the development of the PIA Framework. These
examples reflect the rationale behind and evolution of the text's methods and definitions. The
chapter also provides insight into the stakeholder debates and compromises that have
important implications for PIAs in general.Series: Working Papers on Information Systems, Information Business and Operation
A framework to detect cyber-attacks against networked medical devices (Internet of Medical Things):an attack-surface-reduction by design approach
Most medical devices in the healthcare system are not built-in security concepts. Hence, these devices' built-in vulnerabilities prone them to various cyber-attacks when connected to a hospital network or cloud. Attackers can penetrate devices, tamper, and disrupt services in hospitals and clinics, which results in threatening patients' health and life. A specialist can Manage Cyber-attacks risks by reducing the system's attack surface. Attack surface analysis, either as a potential source for exploiting a potential vulnerability by attackers or as a medium to reduce cyber-attacks play a significant role in mitigating risks. Furthermore, it is necessitated to perform attack surface analysis in the design phase. This research proposes a framework that integrates attack surface concepts into the design and development of medical devices. Devices are classified as high-risk, medium-risk, and low-risk. After risk assessment, the employed classification algorithm detects and analyzes the attack surfaces. Accordingly, the relevant adapted security controls will be prompted to hinder the attack. The simulation and evaluation of the framework is the subject of further research.</p
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Cybersecurity Vulnerabilities in Medical Devices: A Complex Environment and Multifaceted Problem
The increased connectivity to existing computer networks has exposed medical devices to cybersecurity vulnerabilities from which they were previously shielded. For the prevention of cybersecurity incidents, it is important to recognize the complexity of the operational environment as well as to catalog the technical vulnerabilities. Cybersecurity protection is not just a technical issue; it is a richer and more intricate problem to solve. A review of the factors that contribute to such a potentially insecure environment, together with the identification of the vulnerabilities, is important for understanding why these vulnerabilities persist and what the solution space should look like. This multifaceted problem must be viewed from a systemic perspective if adequate protection is to be put in place and patient safety concerns addressed. This requires technical controls, governance, resilience measures, consolidated reporting, context expertise, regulation, and standards. It is evident that a coordinated, proactive approach to address this complex challenge is essential. In the interim, patient safety is under threat
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