-
Cyberpsychology, Behavior and Social... Jul 2018The study contributes to the ongoing debate about the "privacy paradox" in the context of using social media. The presence of a privacy paradox is often declared if...
The study contributes to the ongoing debate about the "privacy paradox" in the context of using social media. The presence of a privacy paradox is often declared if there is no relationship between users' information privacy concerns and their online self-disclosure. However, prior research has produced conflicting results. The novel contribution of this study is that we consider public and private self-disclosure separately. The data came from a cross-national survey of 1,500 Canadians. For the purposes of the study, we only examined the subset of 545 people who had at least one public account and one private account. Going beyond a single view of self-disclosure, we captured five dimensions of self-disclosure: Amount, Depth, Polarity, Accuracy, and Intent; and two aspects of privacy concerns: concerns about organizational and social threats. To examine the collected data, we used Partial Least Squares Structural Equation Modeling. Our research does not support the presence of a privacy paradox as we found a relationship between privacy concerns from organizational and social threats and most of the dimensions of self-disclosure (even if the relationship was weak). There was no difference between patterns of self-disclosure on private versus public accounts. Different privacy concerns may trigger different privacy protection responses and, thus, may interact with self-disclosure differently. Concerns about organizational threats increase awareness and accuracy while reducing amount and depth, while concerns about social threats reduce accuracy and awareness while increasing amount and depth.
Topics: Adult; Canada; Confidentiality; Female; Humans; Intention; Male; Middle Aged; Privacy; Self Disclosure; Social Media; Young Adult
PubMed: 29995525
DOI: 10.1089/cyber.2017.0709 -
Studies in Health Technology and... Nov 2022The population aging has facilitated a growing number of welfare technologies and smart home solutions. These technologies enable clinical staff and health care...
The population aging has facilitated a growing number of welfare technologies and smart home solutions. These technologies enable clinical staff and health care professionals to provide health services in an intelligent way with the trend of patient-centric digital health platforms. As one of the health services, response center service is facing new challenges when connected with welfare technologies, such as false alarms, security threats, privacy leakage, etc. This paper introduces the mechanism of the response center and the role it plays in healthcare. We conduct an exploratory study to find out the benefits and challenges of the response center service from the results of a structured interview. Based on the findings, we identify the required services to improve the intelligent response center mechanism.
Topics: Humans; Delivery of Health Care; Privacy; Health Services; Home Care Services
PubMed: 36325846
DOI: 10.3233/SHTI220963 -
Big Data Jun 2021The Recommendation system relies on feedback and personal information collected from users for effective recommendation. The success of a recommendation system is highly...
The Recommendation system relies on feedback and personal information collected from users for effective recommendation. The success of a recommendation system is highly dependent on storing and managing sensitive customer information. Users refrain from using the application if there is a threat to user privacy. Several works that were performed to protect user privacy have paid little attention to utility. Hence, there is a need for a robust recommendation system with high accuracy and privacy. Model-based approaches are more prevalent and commonly used in recommendation. The proposed work improvises the existing private model-based collaborative filtering algorithm with high privacy and utility. We identified that data sparsity is the primary reason for most of the threats in a recommender framework through an extensive literature survey. Hence, our approach combines the injection for imputing the missing ratings, which are deemed low, with differential privacy. We additionally introduce a random differential privacy approach to alternating least square (ALS) for improved utility. Experimental results on benchmarked datasets confirm that the performance of our private noisy Random ALS algorithm outperforms the non-noisy ALS for all datasets.
Topics: Algorithms; Privacy
PubMed: 33739861
DOI: 10.1089/big.2020.0038 -
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 -
BMC Medical Informatics and Decision... Aug 2021Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy...
BACKGROUND
Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches.
METHODS
The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes.
RESULTS
Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility.
CONCLUSIONS
There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.
Topics: Biomedical Research; Computer Security; Humans; Information Dissemination; Privacy
PubMed: 34384406
DOI: 10.1186/s12911-021-01602-x -
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 -
IEEE Transactions on Bio-medical... Nov 2022Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been frequently...
OBJECTIVE
Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been frequently used to improve the accuracy of EEG-based BCIs for a new user (target domain), by making use of labeled data from a previous user (source domain). However, this raises privacy concerns, as EEG contains sensitive health and mental information. It is very important to perform privacy-preserving domain adaptation, which simultaneously improves the classification accuracy for a new user and protects the privacy of a previous user.
METHODS
We propose augmentation-based source-free adaptation (ASFA), which consists of two parts: 1) source model training, where a novel data augmentation approach is proposed for MI EEG signals to improve the cross-subject generalization performance of the source model; and, 2) target model training, which simultaneously considers uncertainty reduction for domain adaptation and consistency regularization for robustness. ASFA only needs access to the source model parameters, instead of the raw EEG data, thus protecting the privacy of the source domain. We further extend ASFA to a stricter privacy-preserving scenario, where the source model's parameters are also inaccessible.
RESULTS
Experimental results on four MI datasets demonstrated that ASFA outperformed 15 classical and state-of-the-art MI classification approaches.
SIGNIFICANCE
This is the first work on completely source-free domain adaptation for EEG-based BCIs. Our proposed ASFA achieves high classification accuracy and strong privacy protection simultaneously, important for the commercial applications of EEG-based BCIs.
Topics: Brain-Computer Interfaces; Privacy; Electroencephalography; Imagination; Algorithms
PubMed: 35439124
DOI: 10.1109/TBME.2022.3168570 -
PloS One 2022The security of the tax system is directly related to the development of a country. The conventional process of tax payment laborious steps, so this process becomes a...
The security of the tax system is directly related to the development of a country. The conventional process of tax payment laborious steps, so this process becomes a cause of irregularities among taxpayers and tax authorities, increasing the rate of corruption in tax collection. Blockchain, as a distributed ledger technology, its unique advantages and promising applications in taxation offer an effective solution to the problems of electronic taxation. However, the transparency of blockchain exists the risk of privacy disclosure, the high degree of anonymity brings the problem of lack of user supervision. Therefore, for balancing the contradiction of taxpayer privacy and supervision, we propose a blockchain-based self-certified and anonymous e-taxing scheme, which uses blockchain as the underlying support, and utilizes cryptography technology such as self-certified public key, Diffie-Hellman, to reduce the taxpayer's reliance on the certificate authority, and protects the taxpayer's anonymity while realizing the tracking of the real identity of malicious taxpayers. The security analysis proves that the scheme has the properties such as anonymity, conditional privacy and unforgeability, etc. Finally, performance analysis shows that compared with similar schemes, the scheme significantly improves the registration efficiency, proving its practicability and implementability.
Topics: Blockchain; Privacy; Taxes; Technology
PubMed: 35789334
DOI: 10.1371/journal.pone.0270454