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The Journal of Law, Medicine & Ethics :... Mar 2020At one time, specialized health privacy laws represented the bulk of the rules regulating genetic privacy, Today, however, as both the field of genomics and the content...
At one time, specialized health privacy laws represented the bulk of the rules regulating genetic privacy, Today, however, as both the field of genomics and the content of privacy law change rapidly, a new generation of general-purpose privacy laws may impose new restrictions on collection, storage, and disclosure of genetic data. This article surveys these laws and considers implications.
Topics: Confidentiality; Genetic Privacy; Genomics; Government Regulation; Humans; Privacy
PubMed: 32342776
DOI: 10.1177/1073110520917002 -
Computational Intelligence and... 2022Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational...
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals' personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users' privacy.
Topics: Artificial Intelligence; Educational Technology; Humans; Machine Learning; Privacy; Technology
PubMed: 35469207
DOI: 10.1155/2022/3502992 -
Science and Engineering Ethics Apr 2019This article proposes a new definition of information security, the 'Appropriate Access' definition. Apart from providing the basic criteria for a definition-correct...
This article proposes a new definition of information security, the 'Appropriate Access' definition. Apart from providing the basic criteria for a definition-correct demarcation and meaning concerning the state of security-it also aims at being a definition suitable for any information security perspective. As such, it bridges the conceptual divide between so-called 'soft issues' of information security (those including, e.g., humans, organizations, culture, ethics, policies, and law) and more technical issues. Because of this it is also suitable for various analytical purposes, such as analysing possible security breaches, or for studying conflicting attitudes on security in an organization. The need for a new definition is demonstrated by pointing to a number of problems for the standard definition type of information security-the so-called CIA definition. Besides being too broad as well as too narrow, it cannot properly handle the soft issues of information security, nor recognize the contextual and normative nature of security.
Topics: Access to Information; Computer Security; Confidentiality; Ethics; Humans; Information Storage and Retrieval; Policy; Privacy
PubMed: 29143269
DOI: 10.1007/s11948-017-9992-1 -
Bundesgesundheitsblatt,... Feb 2024Broad access to health data offers great potential for science and research. However, health data often contains sensitive information that must be protected in a... (Review)
Review
Broad access to health data offers great potential for science and research. However, health data often contains sensitive information that must be protected in a special way. In this context, the article deals with the re-identification potential of health data. After defining the relevant terms, we discuss factors that influence the re-identification potential. We summarize international privacy standards for health data and highlight the importance of background knowledge. Given that the reidentification potential is often underestimated in practice, we present strategies for mitigation based on the Five Safes concept. We also discuss classical data protection strategies as well as methods for generating synthetic health data. The article concludes with a brief discussion and outlook on the planned Health Data Lab at the Federal Institute for Drugs and Medical Devices.
Topics: Germany; Privacy; Computer Security; Confidentiality
PubMed: 38231225
DOI: 10.1007/s00103-023-03820-2 -
Sensors (Basel, Switzerland) Oct 2022With the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged...
With the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC, and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage, and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, which is justifiable considering the privacy and security provided with the architecture and encryption.
Topics: Blockchain; Privacy; Delivery of Health Care; Algorithms; Technology; Computer Security
PubMed: 36365991
DOI: 10.3390/s22218292 -
Sensors (Basel, Switzerland) Jan 2023As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system... (Review)
Review
As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system elements and their communication paths could be protected individually, generic, ecosystem-wide approaches were sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains came into the technological limelight. Blockchains offer many advantageous features in relation to enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain technology can provide IoT with effective and efficient solutions, there are many challenges related to various aspects of integrating these technologies. While security, anonymity/data privacy, and smart contract-related features are apparently advantageous for blockchain technologies (BCT), there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate scalability, predictability, and legal issues. This paper provides a systematic review on state-of-the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and privacy-related issues. The paper first provides a brief overview of BCT and IoT's basic principles, including their architecture, protocols and consensus algorithms, characteristics, and the challenges of integrating them. Afterwards, it describes the survey methodology, including the search strategy, eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the findings of this study which illustrates different works that addressed the integration of blockchain technology and IoT to tackle various aspects of privacy and security, which are followed by a categorization of applications that have been investigated with different characteristics, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is included from an architectural and operational perspective. Finally, we cover major gaps and future considerations that can be taken into account when integrating blockchain technology with IoT.
Topics: Blockchain; Ecosystem; Internet of Things; Privacy; Technology; Computer Security
PubMed: 36679582
DOI: 10.3390/s23020788 -
Sensors (Basel, Switzerland) Sep 2016With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to...
With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.
Topics: Algorithms; Computer Communication Networks; Computer Security; Data Collection; Medical Informatics; Privacy
PubMed: 27626417
DOI: 10.3390/s16091463 -
Sensors (Basel, Switzerland) Aug 2021Edge computing has been introduced to the Internet of Things (IoT) to meet the requirements of IoT applications. At the same time, data aggregation is widely used in...
Edge computing has been introduced to the Internet of Things (IoT) to meet the requirements of IoT applications. At the same time, data aggregation is widely used in data processing to reduce the communication overhead and energy consumption in IoT. Most existing schemes aggregate the overall data without filtering. In addition, aggregation schemes also face huge challenges, such as the privacy of the individual IoT device's data or the fault-tolerant and lightweight requirements of the schemes. In this paper, we present a privacy-preserving and lightweight selective aggregation scheme with fault tolerance (PLSA-FT) for edge computing-enhanced IoT. In PLSA-FT, selective aggregation can be achieved by constructing Boolean responses and numerical responses according to specific query conditions of the cloud center. Furthermore, we modified the basic Paillier homomorphic encryption to guarantee data privacy and support fault tolerance of IoT devices' malfunctions. An online/offline signature mechanism is utilized to reduce computation costs. The system characteristic analyses prove that the PLSA-FT scheme achieves confidentiality, privacy preservation, source authentication, integrity verification, fault tolerance, and dynamic membership management. Moreover, performance evaluation results show that PLSA-FT is lightweight with low computation costs and communication overheads.
Topics: Algorithms; Computer Security; Confidentiality; Internet of Things; Privacy
PubMed: 34450808
DOI: 10.3390/s21165369 -
The American Journal of Bioethics : AJOB Jul 2022
Topics: Humans; Privacy
PubMed: 35737483
DOI: 10.1080/15265161.2022.2075975 -
Sensors (Basel, Switzerland) Jan 2022The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to... (Review)
Review
The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.
Topics: Cloud Computing; Computer Security; Ecosystem; Privacy; Surveys and Questionnaires
PubMed: 35161675
DOI: 10.3390/s22030927