Results 11 to 20 of about 23,626,077 (284)
Data anonymization patent landscape [PDF]
Croatian Operational Research Review, 2017 The omnipresent, unstoppable increase in digital data has led to a greater understanding of the importance of data privacy. Different approaches are used to implement data privacy.
Mirjana Pejić Bach +2 more
doaj +2 more sources
Utility-preserving anonymization for health data publishing
BMC Medical Informatics and Decision Making, 2017 Background Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information.
Hyukki Lee +3 more
doaj +2 more sources
Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix [PDF]
, 2018 Background: The current law on anonymization sets the same standard across all situations, which poses a problem for biomedical research. Objective: We propose a matrix for setting different standards, which is responsive to context and public ...
John M. M. Rumbold (16674570) +1 more
core +7 more sources
Anonymization Procedures for Tabular Data: An Explanatory Technical and Legal Synthesis
Information, 2023 In the European Union, Data Controllers and Data Processors, who work with personal data, have to comply with the General Data Protection Regulation and other applicable laws. This affects the storing and processing of personal data.
Robert Aufschläger +6 more
doaj +1 more source
Anonymizing Temporal Data [PDF]
2010 IEEE International Conference on Data Mining, 2010 Temporal data are time-critical in that the snapshot at each timestamp must be made available to researchers in a timely fashion. However, due to the limited data, each snapshot likely has a skewed distribution on sensitive values, which renders classical anonymization methods not possible.
Ke Wang 0001 +3 more
openaire +2 more sources
Finding the Sweet Spot for Data Anonymization: A Mechanism Design Perspective
IEEE Access, 2022 Data sharing between different organizations is an essential process in today’s connected world. However, recently there were many concerns about data sharing as sharing sensitive information can jeopardize users’ privacy.
Abdelrahman Eldosouky +3 more
doaj +1 more source
A decision-support framework for data anonymization with application to machine learning processes [PDF]
, 2022 The application of machine learning techniques to large and distributed data archives might result in the disclosure of sensitive information about the data subjects. Data often contain sensitive identifiable information, and even if these are protected,
Polese, Giuseppe +10 more
core +2 more sources
Scalable Distributed Data Anonymization [PDF]
, 2021 We present an approach for enabling a distributed anonymization process over large collections of sensor data. Our approach anonymizes large datasets (which might not fit in main memory) using an arbitrary number of workers within the Spark framework. We
Paraboschi, Stefano +19 more
core +1 more source
Spectral Anonymization of Data [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2010 The goal of data anonymization is to allow the release of scientifically useful data in a form that protects the privacy of its subjects. This requires more than simply removing personal identifiers from the data, because an attacker can still use auxiliary information to infer sensitive individual information.
Thomas A. Lasko, Staal Amund Vinterbo
openaire +2 more sources
Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients
Scientific Data, 2022 Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks.
Carolin E. M. Koll +26 more
doaj +1 more source