Protection of Location Privacy using Dummies for Location-based Services
By Hidetoshi Kido, Yutaka Yanagisawa, Tetsuji Satoh. 2005
287 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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
- Konstantinos Chatzikokolakis
- Catuscia Palamidessi
- Nicolás Emilio Bordenabe
- Yong Li
- Zhen Tu
- Xiaoming Fu
- Miguel E. Andrés
- Fengli Xu
In Sociology and Political Science
- Konstantinos Chatzikokolakis
- Catuscia Palamidessi
- Nicolás Emilio Bordenabe
- Zhen Tu
- Xiaoming Fu
- Miguel E. Andrés
- Fengli Xu
- Pengyu Zhang
Citations in the corpus
- On Safeguarding Privacy and Security in the Framework of Federated Learning (2019, neutral): “• Dummy: The concept of dummy method stems from the location privacy protection [11].”
- Broadening the Scope of Differential Privacy Using Metrics (2013, background): “Others works [22,23,24] rely on the well-known concept of k -anonymity.”
- Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases (2008, unknown): “Contrary to the notions of mixed zones and sensitivity maps, the approach introduced in [1] is geared on the concept of location based quasi-identifier, i.e., a spatio-temporal pattern that can uniquely identify one individual.”
- Geo-indistinguishability: differential privacy for location-based systems (2012, background): “Counter-measures are often employed to avoid this issue: for instance, [25] takes into account concepts such as ubiquity, congestion and uniformity for generating dummy points, in an effort to make them look realistic.”
- Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data (2017, background): “…have demonstrated that the privacy risk of releasing anonymized human mobility datasets is severe, since a very high percentage of individuals can be re-identified with their spatial (only locations) [17, 18, 33, 34, 35] or spatiotemporal (with both locations and time information) records[21].”
- Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “Another technique is based on adding dummy locations[31, 32, 5] to the request sent to the service provider.”