Protecting privacy using k-anonymity.

By K. El Emam, Fida Kamal Dankar. 2008

335 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Public Health, Environmental and Occupational Health, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bingsheng He
2. Qinbin Li
3. Virginia Smith
4. Tian Li
5. Ameet Talwalkar
6. Anit Kumar Sahu
7. Zeyi Wen
8. Zhaomin Wu

In Public Health, Environmental and Occupational Health

1. Fida Kamal Dankar
2. K. El Emam

In Sociology and Political Science

1. Fida Kamal Dankar
2. K. El Emam
3. Virginia Smith
4. Tian Li
5. Luigi Atzori
6. Sabrina Sicari

Citations in the corpus

• Federated Learning: Challenges, Methods, and Future Directions (2019, background): “Despite the fact that differential privacy is the de facto metric for privacy in machine learning, there are many other privacy definitions, such as k-anonymity [15] and d-presence [34], which may be applicable to different learning problems.”
• Preparing Medical Imaging Data for Machine Learning (2020, background): “Finally, medical data can be anonymized with k-anonymity, which transforms an original data set containing protected health information to prevent potential intruders from determining the patient’s identity (33).”
• A Critical Analysis on the Security Concerns of Internet of Things (IoT) (2015, methodology): “As for hiding the sensitive information, anonymity of the location and identity is obtained using K-Anonymity approach which ensures the protection of the information like identity and location etc of the user [27].”
• A method for evaluating marketer re-identification risk (2010, methodology): “Previous risk measures focused mostly on the case where a single patient is being re-identified [3].”
• Information security and privacy in healthcare: current state of research (2010, neutral): “…for such disclosures that ensure balanced trade-off between disclosure risk and information loss, and estimation of population size cut-off at which data suppression could prove fruitful strategy for data disclosure (El Emam, 2008; El Emam and Dankar, 2008; El Emam et al., 2006, 2007, 2009).”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “Moreover, there are many privacy mechanisms such as differential privacy [45] and k-anonymity [47], which provide different privacy guarantees.”