Results 251 to 260 of about 24,103,638 (294)

Data Anonymization Techniques

Removing identifiable information from research data is important to maintain the privacy of research participants. Unfortunately, this is often not enough to prevent deanonymization when the dataset is combined with other data. This recommendation shows some countermeasures against deanonymization and how to apply them.
openaire   +1 more source

Anonymous Source of Data

2017
Academic writers have the ethical responsibility to protect their sources of data, to inform participants of how personal information will be secured, and to inform participants when anonymity cannot be safeguarded. The informed consent document, reviewed prior to data collection, conveys the appropriate information to potential participants.
openaire   +2 more sources

Innovations in research and clinical care using patient‐generated health data

Ca-A Cancer Journal for Clinicians, 2020
H S L Jim, Aasha I Hoogland, Naomi C Brownstein   +2 more
exaly  

Data Anonymization

2015
Josep Domingo-Ferrer, Jordi Soria-Comas
openaire   +1 more source

Amnesia - Data anonymization

The BY-COVID project works towards enabling and improving the accessibility of COVID-19 and other infectious disease data to researchers, policy-makers, and the public. The BY-COVID Fest took place on 23-25 January 2024 in Athens, Greece, as the final event in a series of training events on Research Data Management (RDM) and the General Data Protection
openaire   +1 more source

Cancer statistics, 2020

Ca-A Cancer Journal for Clinicians, 2020
Rebecca L Siegel, Kimberly D Miller
exaly  

Movement data anonymity through generalization

2010
Wireless networks and mobile devices, such as mobile phones and GPS receivers, sense and track the movements of people and vehicles, producing society-wide mobility databases. This is a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out of discovering new knowledge about human mobile behavior, and thus ...
Monreale A, Andrienko G, Andrienko N, Giannotti F, Pedreschi D, Rinzivillo S, Wrobel S   +6 more
openaire   +3 more sources

Anonymous sequences from trajectory data

2009
The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data.
R. G. Pensa, MONREALE, ANNA, F. Pinelli, PEDRESCHI, DINO   +3 more
openaire   +4 more sources

Data anonymization: a novel optimal k-anonymity algorithm for identical generalization hierarchy data in IoT

Service Oriented Computing and Applications, 2020
Waranya Mahanan, W. Chaovalitwongse, J. Natwichai   +2 more
semanticscholar   +1 more source

Graph Data Anonymization, De-Anonymization Attacks, and De-Anonymizability Quantification: A Survey

IEEE Communications Surveys and Tutorials, 2017
S. Ji, Prateek Mittal, R. Beyah
semanticscholar   +1 more source