scholarly journals Quantifying Interdependent Risks in Genomic Privacy

2017 ◽  
Vol 20 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Mathias Humbert ◽  
Erman Ayday ◽  
Jean-Pierre Hubaux ◽  
Amalio Telenti
Keyword(s):  
Genomic Privacy ◽  
Interdependent Risks

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 059-059
Keyword(s):  
Large Scale ◽  
Biomedical Data ◽  
Theoretic Approach ◽  
Privacy And Security ◽  
Data Repositories ◽  
Sexually Transmitted ◽  
Game Theoretic ◽  
Genomic Privacy ◽  
Interdependent Risks ◽  
Game Theoretic Approach

Humbert M, Ayday E, Hubaux JP, Telenti A. Quantifying Interdependent Risks in Genomic Privacy. ACM Transactions on Privacy and Security 2017;20(1):3 https://dl.acm.org/citation.cfm?id=3035538 Yuan J, Malin B, Modave F, Guo Y, Hogan WR, Shenkman E, Bian J. Towards a privacy preserving cohort discovery framework for clinical research networks. J Biomed Inform 2017;66:42-51 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5316314/ Ohno-Machado L, Sansone SA, Alter G, Fore I, Grethe J, Xu H, Gonzalez-Beltran A, Rocca-Serra P, Guraraj AE, Bell E, Soysal E, Zong N, Kim HE. Finding useful data across multiple biomedical data repositories using DataMed. Nat Genet 2017;49(6):816-9 https://www.nature.com/articles/ng.3864 Wan Z, Vorobeychik Y, Xia W, Clayton EW, Kantarcioglu M, Malin B. Expanding access to large-scale genomic data while promoting privacy: a game theoretic approach. Am J Hum Genet 2017;100(2):316-22 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5294764/ Gilbert M, Bonnell A, Farrell J, Haag D, Bondyra M, Unger D, Elliot E. Click yes to consent: incorporating informed consent into an internet-based testing program for sexually transmitted and blood-borne infections. Int J Med Inform 2017;105:38-48 https://www.sciencedirect.com/science/article/pii/S1386505617301697?via%3Dihub

scholarly journals Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

2021 ◽  
Vol 24 (2) ◽  
pp. 1-35
Author(s):  
Isabel Wagner ◽  
Iryna Yevseyeva
Keyword(s):  
Optimization Problem ◽  
Evolutionary Optimization ◽  
Multiple Objectives ◽  
Shared Value ◽  
Vehicular Communications ◽  
Open Problems ◽  
Value Range ◽  
Genomic Privacy ◽  
Metaheuristic Optimization Algorithms ◽  
Selection Of

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

Project portfolio risk analysis with the consideration of project interdependencies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Libiao Bai ◽  
Huijing Shi ◽  
Shuyun Kang ◽  
Bingbing Zhang
Keyword(s):  
Bayesian Network ◽  
Evaluation Model ◽  
Project Portfolio ◽  
Market Demand ◽  
Portfolio Risk ◽  
Project Schedule ◽  
High Stake ◽  
Content Type ◽  
Project Portfolios ◽  
Interdependent Risks

PurposeComprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.Design/methodology/approachIn this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.FindingsThe interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.Originality/valueThe findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.

Protecting Genomic Sequence Anonymity with Generalization Lattices

2005 ◽  
Vol 44 (05) ◽  
pp. 687-692 ◽  
Author(s):  
B. A. Malin
Keyword(s):  
Dna Sequences ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Personal Information ◽  
Control Technique ◽  
Single Nucleotide ◽  
Specific Data ◽  
Disclosure Control ◽  
Genomic Privacy ◽  
Nucleotide Region

Summary Objectives: Current genomic privacy technologies assume the identity of genomic sequence data is protected if personal information, such as demographics, are obscured, removed, or encrypted. While demographic features can directly compromise an individual’s identity, recent research demonstrates such protections are insufficient because sequence data itself is susceptible to re-identification. To counteract this problem, we introduce an algorithm for anonymizing a collection of person-specific DNA sequences. Methods: The technique is termed DNA lattice an-onymization (DNALA), and is based upon the formal privacy protection schema of k-anonymity. Under this model, it is impossible to observe or learn features that distinguish one genetic sequence from k-1 other entries in a collection. To maximize information retained in protected sequences, we incorporate a concept generalization lattice to learn the distance between two residues in a single nucleotide region. The lattice provides the most similar generalized concept for two residues (e.g. adenine and guanine are both purines). Results: The method is tested and evaluated with several publicly available human population datasets ranging in size from 30 to 400 sequences. Our findings imply the anonymization schema is feasible for the protection of sequences privacy. Conclusions: The DNALA method is the first computational disclosure control technique for general DNA sequences. Given the computational nature of the method, guarantees of anonymity can be formally proven. There is room for improvement and validation, though this research provides the groundwork from which future researchers can construct genomics anonymization schemas tailored to specific data-sharing scenarios.

Ensuring privacy and security of genomic data and functionalities

2019 ◽  
Vol 21 (2) ◽  
pp. 511-526 ◽  
Author(s):  
Abukari Mohammed Yakubu ◽  
Yi-Ping Phoebe Chen
Keyword(s):  
Association Studies ◽  
Diagnostic Testing ◽  
Healthcare Delivery ◽  
Genomic Data ◽  
Security And Privacy ◽  
Genome Wide Association Studies ◽  
Privacy And Security ◽  
Security Issues ◽  
Genomic Privacy ◽  
Privacy Problem

Abstract In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities—computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

scholarly journals HeLa Sequencing and Genomic Privacy: The Next Chapter

2013 ◽  
Vol 3 (8) ◽  
pp. vii-vii
Author(s):  
Brenda J. Andrews ◽  
Tracey DePellegrin
Keyword(s):  
Genomic Privacy

scholarly journals Open Imputation Server provides secure Imputation services with provable genomic privacy

2021 ◽  
Author(s):  
Arif Ozgun Harmanci ◽  
Miran Kim ◽  
Su Wang ◽  
Wentao Li ◽  
Yongsoo Song ◽  
...  
Keyword(s):  
Genomic Data ◽  
Genotype Imputation ◽  
Genotype Data ◽  
Sequencing Data ◽  
Encrypted Data ◽  
Client Server ◽  
Personal Use ◽  
Genomic Data Analysis ◽  
Genomic Privacy ◽  
Computational Resources

As DNA sequencing data is available for personal use, genomic privacy is becoming a major challenge. Nevertheless, high-throughput genomic data analysis outsourcing is performed using pipelines that tend to overlook these challenges. Results: We present a client-server-based outsourcing framework for genotype imputation, an important step in genomic data analyses. Genotype data is encrypted by the client and encrypted data are used by the server that never observes the data in plain. Cloud-based framework can benefit from virtually unlimited computational resources while providing provable confidentiality. Availability: Server is publicly available at https://www.secureomics.org/OpenImpute. Users can anonymously test and use imputation server without registration.

scholarly journals Inference Attacks against Kin Genomic Privacy

2017 ◽  
Vol 15 (5) ◽  
pp. 29-37 ◽  
Author(s):  
Erman Ayday ◽  
Mathias Humbert
Keyword(s):  
Inference Attacks ◽  
Genomic Privacy
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