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Sensors (Basel, Switzerland) Dec 2022In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks...
In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.
Topics: Privacy; Communication; Computers, Handheld; Information Technology; Policy
PubMed: 36616692
DOI: 10.3390/s23010095 -
Briefings in Bioinformatics Nov 2022Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to...
BACKGROUND
Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.
RESULTS
Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.
CONCLUSIONS
Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.
SHORT ABSTRACT
Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.
Topics: Humans; Genome-Wide Association Study; Privacy; Genotype; Genetic Privacy; Genome
PubMed: 36384083
DOI: 10.1093/bib/bbac473 -
Yearbook of Medical Informatics Aug 2018To summarize notable research contributions published in 2017 on data sharing and privacy issues in medical informatics. (Review)
Review
OBJECTIVE
To summarize notable research contributions published in 2017 on data sharing and privacy issues in medical informatics.
METHODS
An extensive search of PubMed/Medline, Web of Science, ACM Digital Library, IEEE Xplore, and AAAI Digital Library was conducted to uncover the scientific contributions published in 2017 that addressed issues of biomedical data sharing, with a focus on data access and privacy. The selection process was based on three steps: (i) a selection of candidate best papers, (ii) the review of the candidate best papers by a team of international experts with respect to six predefined criteria, and (iii) the selection of the best papers by the editorial board of the Yearbook Results: Five best papers were selected. They cover the lifecycle of biomedical data collection, use, and sharing. The papers introduce 1) consenting strategies for emerging environments, 2) software for searching and retrieving datasets in organizationally distributed environments, 3) approaches to measure the privacy risks of sharing new data increasingly utilized in research and the clinical setting (e.g., genomic), 4) new cryptographic techniques for querying clinical data for cohort discovery, and 5) novel game theoretic strategies for publishing summary information about genome-phenome studies that balance the utility of the data with potential privacy risks to the participants of such studies.
CONCLUSION
The papers illustrated that there is no one-size-fitsall solution to privacy while working with biomedical data. At the same time, the papers show that there are opportunities for leveraging newly emerging technologies to enable data use while minimizing privacy risks.
Topics: Access to Information; Confidentiality; Information Dissemination; Medical Informatics; Privacy
PubMed: 30157505
DOI: 10.1055/s-0038-1641216 -
Critical Care Medicine Mar 2017To review the legal issues concerning family members' access to information when patients are in the ICU. (Review)
Review
OBJECTIVE
To review the legal issues concerning family members' access to information when patients are in the ICU.
DATA SOURCES
U.S. Code, U.S. Code of Federal Regulations, and state legislative codes.
DATA EXTRACTION
Relevant legal statutes and regulations were identified and reviewed by the two attorney authors (L. F., M. A. V.).
STUDY SELECTION
Not applicable.
DATA SYNTHESIS
Review by all coauthors.
CONCLUSIONS
The Health Insurance Portability and Accountability Act and related laws should not be viewed as barriers to clinicians sharing information with ICU patients and their loved ones. Generally, under Health Insurance Portability and Accountability Act, personal representatives have the same authority to receive information that patients would otherwise have. Persons involved in the patient's care also may be given information relevant to the episode of care unless the patient objects. ICUs should develop policies for handling the issues we identify about such information sharing, including policies for responding to telephone inquiries and methods for giving patients the opportunity to object to sharing information with individuals involved in their care. ICU clinicians also should be knowledgeable of their state's laws about how to identify patients' personal representatives and the authority of those representatives. Finally, ICU clinicians should be aware of any special restrictions their state places on medical information. In aggregate, these strategies should help ICU managers and clinicians facilitate robust communication with patients and their loved ones.
Topics: Access to Information; Communication; Family; Health Insurance Portability and Accountability Act; Humans; Intensive Care Units; Organizational Policy; Patient Preference; Privacy; United States
PubMed: 27922454
DOI: 10.1097/CCM.0000000000002190 -
Medical Image Analysis May 2024Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image...
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we propose several techniques to optimize the image registration operations by using gradient approximations, and by revisiting the use of homomorphic encryption trough packing, to allow the efficient encryption and multiplication of large matrices. We focus on registration methods of increasing complexity, including rigid, affine, and non-linear registration based on cubic splines or diffeomorphisms parameterized by time-varying velocity fields. In all these settings, we demonstrate how the registration problem can be naturally adapted for accounting to privacy-preserving operations, and illustrate the effectiveness of PPIR on a variety of registration tasks.
Topics: Humans; Privacy; Computer Security
PubMed: 38471338
DOI: 10.1016/j.media.2024.103129 -
Nature Reviews. Genetics Apr 2022The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research... (Review)
Review
The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.
Topics: Genetic Privacy; Genomics; High-Throughput Nucleotide Sequencing; Privacy; Risk Assessment
PubMed: 34759381
DOI: 10.1038/s41576-021-00428-7 -
Nature Communications Jun 2023Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a...
Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.
Topics: Artificial Intelligence; Big Data; Knowledge; Machine Learning; Privacy
PubMed: 37355643
DOI: 10.1038/s41467-023-38794-x -
Neural Networks : the Official Journal... Jun 2024Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across... (Review)
Review
Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.
Topics: Benchmarking; Knowledge; Privacy
PubMed: 38490115
DOI: 10.1016/j.neunet.2024.106230 -
JMIR Nursing May 2024Health monitoring technologies help patients and older adults live better and stay longer in their own homes. However, there are many factors influencing their adoption... (Review)
Review
BACKGROUND
Health monitoring technologies help patients and older adults live better and stay longer in their own homes. However, there are many factors influencing their adoption of these technologies. Privacy is one of them.
OBJECTIVE
The aim of this study was to provide an overview of the privacy barriers in health monitoring from current research, analyze the factors that influence patients to adopt assisted living technologies, provide a social psychological explanation, and propose suggestions for mitigating these barriers in future research.
METHODS
A scoping review was conducted, and web-based literature databases were searched for published studies to explore the available research on privacy barriers in a health monitoring environment.
RESULTS
In total, 65 articles met the inclusion criteria and were selected and analyzed. Contradictory findings and results were found in some of the included articles. We analyzed the contradictory findings and provided possible explanations for current barriers, such as demographic differences, information asymmetry, researchers' conceptual confusion, inducible experiment design and its psychological impacts on participants, researchers' confirmation bias, and a lack of distinction among different user roles. We found that few exploratory studies have been conducted so far to collect privacy-related legal norms in a health monitoring environment. Four research questions related to privacy barriers were raised, and an attempt was made to provide answers.
CONCLUSIONS
This review highlights the problems of some research, summarizes patients' privacy concerns and legal concerns from the studies conducted, and lists the factors that should be considered when gathering and analyzing people's privacy attitudes.
Topics: Humans; Privacy; Monitoring, Physiologic
PubMed: 38723253
DOI: 10.2196/53592 -
IEEE Journal of Biomedical and Health... Feb 2023Great progress has been made in diagnosing medical diseases based on deep learning. Large-scale medical data are expected to improve deep learning performance further....
Great progress has been made in diagnosing medical diseases based on deep learning. Large-scale medical data are expected to improve deep learning performance further. It is almost impossible for a single institution to collect so much data due to the time-consuming and costly collection and labeling of medical data. Many studies have turned attention to data sharing among multiple medical institutions. However, due to different data acquiring and processing procedures, multiple institutions' medical data is characterized by distribution heterogeneity. Besides, the protection of patient privacy in medical data sharing has also been a common concern. To simultaneously address the problems of heterogeneous data distribution and privacy protection, we propose a novel multi-source source free domain adaptation. When aligning distributed heterogeneous data, our method only require to transfer the pre-trained source models rather than the direct source domain data, thus protecting patients' privacy. In addition, it has the advantages of being efficient and less costly in network resources. The proposed method is evaluated on the multi-site fMRI database Autism Brain Imaging Data Exchange (ABIDE) and yields an average accuracy of 69.37%. We also analyzed its effectiveness on network resource-saving and conducted additional experiments on Camelyon17 to validate the generalization.
Topics: Humans; Privacy; Brain; Databases, Factual; Information Dissemination
PubMed: 35594226
DOI: 10.1109/JBHI.2022.3175071