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Advanced Materials Interfaces, EarlyView.PDMS samples were covalently coated with mucin glycoproteins and then treated with either plasma activated water (PAW) or saline (PAS) as a means of disinfection. The samples could be successfully sterilized without compromising the structural integrity or functionality of those coatings.
Bernardo Miller Naranjo +4 more
wiley +1 more source
A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy. [PDF]
J Am Med Inform AssocChen H +7 more
europepmc +1 more source
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Communications of the ACM, 2021
A discussion with Miguel Guevara, Damien Desfontaines, Jim Waldo, and Terry ...
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A discussion with Miguel Guevara, Damien Desfontaines, Jim Waldo, and Terry ...
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ACM SIGPLAN Notices, 2015
Differential privacy provides a way to get useful information about sensitive data without revealing much about any one individual. It enjoys many nice compositionality properties not shared by other approaches to privacy, including, in particular, robustness against side-knowledge. Designing differentially private mechanisms from scratch can
Hamid Ebadi +2 more
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Differential privacy provides a way to get useful information about sensitive data without revealing much about any one individual. It enjoys many nice compositionality properties not shared by other approaches to privacy, including, in particular, robustness against side-knowledge. Designing differentially private mechanisms from scratch can
Hamid Ebadi +2 more
openaire +1 more source
When Differential Privacy Implies Syntactic Privacy
IEEE Transactions on Information Forensics and Security, 2022Two main privacy models for sanitising datasets are differential privacy (DP) and syntactic privacy . The former restricts individual values’ impact on the output based on the dataset while the latter restructures the dataset before publication to link any record to multiple sensitive data values.
Emelie Ekenstedt +5 more
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Differential Privacy and Security [PDF]
Fundamenta Informaticae, 2016 A quantification of process’s security by differential privacy is defined and studied in the framework of probabilistic process algebras. The resulting (quantitative) security properties are investigated and compared with other (qualitative and quantitative) security notions.
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2006
In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved.
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In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved.
openaire +1 more source
2019
The problem of learning from data while preserving the privacy of individual observations has a long history and spans over multiple disciplines [1, 2, 3]. One way to preserve privacy is to corrupt the learning procedure with noise without destroying the information that we want to extract. Differential Privacy (DP) is one of the most powerful tools in
openaire +2 more sources
The problem of learning from data while preserving the privacy of individual observations has a long history and spans over multiple disciplines [1, 2, 3]. One way to preserve privacy is to corrupt the learning procedure with noise without destroying the information that we want to extract. Differential Privacy (DP) is one of the most powerful tools in
openaire +2 more sources