scholarly journals Genetic Data Privacy Solutions in the GDPR

2019 ◽  
Vol 7 (1) ◽  
pp. 269-297 ◽  
Author(s):  
Kristi Harbord
Keyword(s):  
Data Privacy ◽  
Large Scale ◽  
Genetic Data ◽  
Health Data ◽  
Genetic Privacy ◽  
Private Companies ◽  
Privacy Concerns ◽  
Direct To Consumer ◽  
Consumer Data ◽  
Doctor's Office

The intersection of healthcare and technology is a rapidly growing area. One thriving field at this intersection involves obtaining, processing, and storing genetic data. While the benefits have been great, genetic information can reveal a great deal about individuals and their families. And the information that can be conveyed from genetic data appears limitless and is constantly growing and changing. Many entities have begun storing, processing, and sharing genetic data on a very large scale. This creates many privacy concerns that the current regulatory framework does not account for. The line between patient data and consumer data is blurred; many entities are interested in obtaining genetic data with varied interests. In the direct-to-consumer genetic testing market, consumers pay to send private companies their DNA samples in exchange for a trivial amount of information about their ancestry and health risks. But health data obtained and processed by a company are subjected to far less stringent privacy regulations than health data obtained and processed at a doctor’s office or hospital. This Comment summarizes some of the current genetic privacy problems in United States laws and examines the EU’s recently adopted GDPR for a possible solution. A GDPR-style regulation could provide more consistency, give individuals more control, and protect against future unknown uses.

scholarly journals Privacy, security, and the public health researcher in the era of electronic health record research

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Neal D Goldstein ◽  
Anand D Sarwate
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Large Scale ◽  
Health Data ◽  
Shared Responsibility ◽  
Privacy And Security ◽  
The Public ◽  
Health Researcher ◽  
Public Health Researcher ◽  
Electronic Health

Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.

scholarly journals Trust and The Acquisition and Use of Public Health Information

2021 ◽  
Author(s):  
Stephen Holland ◽  
Jamie Cawthra ◽  
Tamara Schloemer ◽  
Peter Schröder-Bäck
Keyword(s):  
Public Health ◽  
Health Information ◽  
Data Privacy ◽  
Empirical Work ◽  
Personal Data ◽  
Health Data ◽  
Privacy Concerns ◽  
External Threats ◽  
Trust In Institutions ◽  
The Way

AbstractInformation is clearly vital to public health, but the acquisition and use of public health data elicit serious privacy concerns. One strategy for navigating this dilemma is to build 'trust' in institutions responsible for health information, thereby reducing privacy concerns and increasing willingness to contribute personal data. This strategy, as currently presented in public health literature, has serious shortcomings. But it can be augmented by appealing to the philosophical analysis of the concept of trust. Philosophers distinguish trust and trustworthiness from cognate attitudes, such as confident reliance. Central to this is value congruence: trust is grounded in the perception of shared values. So, the way to build trust in institutions responsible for health data is for those institutions to develop and display values shared by the public. We defend this approach from objections, such as that trust is an interpersonal attitude inappropriate to the way people relate to organisations. The paper then moves on to the practical application of our strategy. Trust and trustworthiness can reduce privacy concerns and increase willingness to share health data, notably, in the context of internal and external threats to data privacy. We end by appealing for the sort of empirical work our proposal requires.

scholarly journals Attacks on genetic privacy via uploads to genealogical databases

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Michael D Edge ◽  
Graham Coop
Keyword(s):  
Genetic Information ◽  
Genetic Data ◽  
Proof Of Concept ◽  
Genetic Privacy ◽  
Direct To Consumer ◽  
Multiple Datasets ◽  
Genome Wide ◽  
Genetics Services

Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods by which an adversary can learn database genotypes by uploading multiple datasets. For example, an adversary who uploads approximately 900 genomes could recover at least one allele at SNP sites across up to 82% of the genome of a median person of European ancestries. In databases that detect IBS segments using unphased genotypes, approximately 100 falsified uploads can reveal enough genetic information to allow genome-wide genetic imputation. We provide a proof-of-concept demonstration in the GEDmatch database, and we suggest countermeasures that will prevent the exploits we describe.

scholarly journals Privacy-preserving of patients with Differential Privacy: an experimental evaluation in COVID-19 dataset

2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Manuel E. B. Filho ◽  
Eduardo R. Duarte Neto ◽  
Javam C. Machado
Keyword(s):  
Health Care Providers ◽  
Private Information ◽  
Data Privacy ◽  
Differential Privacy ◽  
Health Data ◽  
Care Providers ◽  
Privacy Concerns ◽  
Effective Strategies ◽  
The World ◽  
New Challenges

The pandemic of the new coronavirus (COVID-19) has brought new challenges to health systems in almost every corner of the world, many of them overburdened. The data analysis has given support in the fight against the coronavirus. Through this analysis, government authorities, together with health care providers, adopted effective strategies. Yet, those strategies can not be careless of privacy concerns. The individuals’ privacy is a right of each citizen. Privacy techniques guarantee the analysis of health data without exposing individuals’ private information. However, a balance between data privacy and utility is essential for a good analysis of the data. This work will demonstrate that it is possible to guarantee the privacy of infected patients and maintain the utility of the data, allowing a sound analysis on them, from the visualization of the application of differentially private mechanisms on queries in the data of patients tested in the State of Ceará - Brazil.

Control Cloud Data Access Privilge Anonymity with Attributed Based Encryption

2017 ◽  
Vol 7 (8) ◽  
pp. 279
Author(s):  
P. Sudheer ◽  
T. Lakshmi Surekha
Keyword(s):  
Data Privacy ◽  
Low Cost ◽  
Data Access ◽  
Privacy Concerns ◽  
Cloud Data ◽  
Computing Paradigm ◽  
Attribute Based Encryption ◽  
Data Content ◽  
Identity Privacy ◽  
Cloud Servers

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The  Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.

Advertising and Price Competition Under Consumer Data Privacy Choices

2020 ◽  
Author(s):  
W. Jason Choi ◽  
Kinshuk Jerath ◽  
Miklos Sarvary
Keyword(s):  
Price Competition ◽  
Data Privacy ◽  
Consumer Data

scholarly journals How to address data privacy concerns when using social media data in conservation science

2021 ◽  
Author(s):  
Enrico Di Minin ◽  
Christoph Fink ◽  
Anna Hausmann ◽  
Jens Kremer ◽  
Ritwik Kulkarni
Keyword(s):  
Social Media ◽  
Data Privacy ◽  
Conservation Science ◽  
Social Media Data ◽  
Privacy Concerns ◽  
Address Data ◽  
Media Data

scholarly journals E-Health & Innovation to Overcome Barriers in Neuromuscular Diseases. Report from the 1st eNMD Congress: Nice, France, March 22-23, 2019

2021 ◽  
pp. 1-12
Author(s):  
Jonathan Pini ◽  
Gabriele Siciliano ◽  
Pauline Lahaut ◽  
Serge Braun ◽  
Sandrine Segovia-Kueny ◽  
...  
Keyword(s):  
Data Privacy ◽  
Neuromuscular Diseases ◽  
Disease Assessment ◽  
Private Companies ◽  
Social Burden ◽  
Database Construction ◽  
Significant Step ◽  
New Perspective ◽  
Expert Community

By definition, neuromuscular diseases are rare and fluctuating in terms of symptoms; patients are often lately diagnosed, do not have enough information to understand their condition and be proactive in their management. Usually, insufficient resources or services are available, leading to patients’ social burden. From a medical perspective, the rarity of such diseases leads to the unfamiliarity of the medical staff and caregiver and an absence of consensus in disease assessment, treatment, and management. Innovations have to be developed in response to patients’ and physicians’ unmet needs. It is vital to improve several aspects of patients’ quality of life with a better comprehension of their disease, simplify their management and follow-up, help their caregiver, and reduce the social and economic burden for living with a rare debilitating disease. Database construction regrouping patients’ data and symptoms according to specific country registration on data privacy will be critical in establishing a clear consensus on neuromuscular disease treatment. Clinicians also need technological innovations to help them recognize neuromuscular diseases, find the best therapeutic approach based on medical consensus, and tools to follow patients’ states regularly. Diagnosis also has to be improved by implementing automated systems to analyze a considerable amount of data, representing a significant step forward to accelerate the diagnosis and the patients’ follow up. Further, the development of new tools able to precisely measure specific outcomes reliably is of the matter of importance in clinical trials to assess the efficacy of a newly developed compound. In this context, the creation of an expert community is essential to communicate and share ideas. To this end, 97 clinicians, healthcare professionals, researchers, and representatives of private companies from 9 different countries met to discuss the new perspective and challenges to develop and implement innovative tools in the field of neuromuscular diseases. Keywords:

scholarly journals Application of Bayesian networks to generate synthetic health data

2020 ◽  
Author(s):  
Dhamanpreet Kaur ◽  
Matthew Sobiesk ◽  
Shubham Patil ◽  
Jin Liu ◽  
Puran Bhagat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Data Privacy ◽  
Statistical Tests ◽  
Synthetic Data ◽  
Original Data ◽  
Health Data ◽  
Minimal Risk ◽  
Data Types ◽  
Automated Method

Abstract Objective This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. Materials and Methods We employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data. Results Our Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. Discussion Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools. Conclusion We conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.

Driving for Big Data? Privacy Concerns in Vehicular Networking

2014 ◽  
Vol 12 (1) ◽  
pp. 77-79 ◽  
Author(s):  
David Eckhoff ◽  
Christoph Sommer
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Concerns ◽  
Vehicular Networking ◽  
Big Data Privacy
Sign in / Sign up

Export Citation Format

Share Document