Results 11 to 20 of about 48,494 (276)

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, Jakob Folz, Elena März, Johann Guggumos, Michael Heigl, Benedikt Buchner, Martin Schramm   +6 more
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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, Yabo Xu, Raymond Chi-Wing Wong, Ada Wai-Chee Fu   +3 more
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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
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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, Tapadhir Das, Anuraag Kotra, Shamik Sengupta   +3 more
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Mobile sensor data anonymization [PDF]

Proceedings of the International Conference on Internet of Things Design and Implementation, 2019
Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or
Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi 0001   +3 more
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Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic [PDF]

, 2020
This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events.
Acien, Alejandro, Fierrez, Julian, Monaco, John V., Morales, Aythami, Ortega-Garcia, Javier, Tolosana, Ruben, Vera-Rodriguez, Ruben   +6 more
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Anonymized data [PDF]

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, 2009
Data anonymization techniques have been the subject of intense investigation in recent years, for many kinds of structured data, including tabular, graph and item set data. They enable publication of detailed information, which permits ad hoc queries and analyses, while guaranteeing the privacy of sensitive information in the data against a variety of ...
Graham Cormode, Divesh Srivastava
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Inferring the Meaning of Non-personal, Anonymized, and Anonymous Data [PDF]

, 2021
On the awareness of the dynamism pertaining to data and its processing, this paper investigates the problem of having two mutually exclusive definitions of personal and non-personal data in the legal framework in force. The taxonomic analysis of key terms and their context of application highlights the risk to crystalize the whole system upon which the
Podda, Emanuela, Palmirani, Monica
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Scalable Distributed Data Anonymization [PDF]

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 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 describe how to parallelize the anonymization process through a proper partitioning of the dataset.
S. De Capitani di Vimercati, D. Facchinetti, S. Foresti, G. Oldani, S. Paraboschi, M. Rossi, P. Samarati   +6 more
openaire   +3 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, Sina M. Pütz, Thierry Meurers, Chin Huang Lee, Mirjam Kohls, Christoph Stellbrink, Charlotte Thibeault, Lennart Reinke, Sarah Steinbrecher, Stefan Schreiber, Lazar Mitrov, Sandra Frank, Olga Miljukov, Johanna Erber, Johannes C. Hellmuth, Jens-Peter Reese, Fridolin Steinbeis, Thomas Bahmer, Marina Hagen, Patrick Meybohm, Stefan Hansch, István Vadász, Lilian Krist, Steffi Jiru-Hillmann, Fabian Prasser, Jörg Janne Vehreschild, NAPKON Study Group   +26 more
doaj   +1 more source