References of "Cogo, Vinicius Vielmo"
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See detailHow can photo sharing inspire sharing genomes?
Cogo, Vinicius Vielmo; Bessani, Alysson; Couto, Francisco M. et al

in 11th International Conference on Practical Applications of Computational Biology & Bioinformatics 2017 (2017)

People usually are aware of the privacy risks of publish-ing photos online, but these risks are less evident when sharing humangenomes. Modern photos and sequenced genomes are both digital rep ... [more ▼]

People usually are aware of the privacy risks of publish-ing photos online, but these risks are less evident when sharing humangenomes. Modern photos and sequenced genomes are both digital rep-resentations of real lives. They contain private information that maycompromise people’s privacy, and still, their highest value is most oftimes achieved only when sharing them with others. In this work, wepresent an analogy between the privacy aspects of sharing photos andsharing genomes, which clarifies the privacy risks in the latter to thegeneral public. Additionally, we illustrate an alternative informed modelto share genomic data according to the privacy-sensitivity level of eachportion. This article is a call to arms for a collaborative work between ge-neticists and security experts to build more effective methods to system-atically protect privacy, whilst promoting the accessibility and sharingof genomes [less ▲]

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See detailA High-Throughput Method to Detect Privacy-Sensitive Human Genomic Data
Cogo, Vinicius Vielmo; Bessani, Alysson; Couto, Francisco M. et al

in Proceedings of the 14th ACM Workshop on Privacy in the Electronic Society (2015)

Finding the balance between privacy protection and data sharing is one of the main challenges in managing human genomic data nowadays. Novel privacy-enhancing technologies are required to address the ... [more ▼]

Finding the balance between privacy protection and data sharing is one of the main challenges in managing human genomic data nowadays. Novel privacy-enhancing technologies are required to address the known disclosure threats to personal sensitive genomic data without precluding data sharing. In this paper, we propose a method that systematically detects privacy-sensitive DNA segments coming directly from an input stream, using as reference a knowledge database of known privacy-sensitive nucleic and amino acid sequences. We show that adding our detection method to standard security techniques provides a robust, efficient privacy-preserving solution that neutralizes threats related to recently published attacks on genome privacy based on short tandem repeats, disease-related genes, and genomic variations. Current global knowledge on human genomes demonstrates the feasibility of our approach to obtain a comprehensive database immediately, which can also evolve automatically to address future attacks as new privacy-sensitive sequences are identified. Additionally, we validate that the detection method can be fitted inline with the NGS---Next Generation Sequencing---production cycle by using Bloom filters and scaling out to faster sequencing machines. [less ▲]

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