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Personal data is shared nowadays in unprecedented frequency, mainly due to the expansion of social networking applications. On the other hand, data privacy is defined to be the ability an individual has to determine what data about him/her can be shared with third parties. For instance, location data may only be processed for the provision of value added service (aka, Location-based Services – LBS), only if such data are anonymized or with the user’s consent. Nevertheless, anonymization is not as simple as it sounds; by removing identifiers, it is not guaranteed that information cannot be de-anonymized (recall the famous Sweeney experiment back in 2001).
The preservation of privacy when either querying protected or publishing sanitized spatiotemporal traces of mobile humans is a field that is receiving growing attention nowadays. We have been working in this field by developing related algorithms, systems, and tools. Research highlights include:
- Hermes++ privacy-aware MOD engine for protected querying over users’ trajectories.
- WCOP (standing for “Who Cares about Others’ Privacy”) personalized anonymity algorithm.
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[av_iconlist_item title=’Selected Publications’ link=” linktarget=” icon=’ue84d’ font=’entypo-fontello’]
| A. Konstantinidis, G. Chatzimilioudis, D. Zeinalipour-Yazti, P. Mpeis, N. Pelekis, Y. Theodoridis: “Privacy-Preserving Indoor Localization on Smartphones”, ICDE/TKDE Poster, Proceedings of the IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, May 2016. IEEE CPS. |
| D. Kopanaki, V. Theodossopoulos, Ν. Pelekis, Ι. Kopanakis, Υ. Theodoridis: “Who Cares about Others’ Privacy: Personalized Anonymization of Moving Object Trajectories”, Proceedings of the 19th International Conference on Extending Database Technology (EDBT2016), Bordeaux, France, March 2016. ACM Press. |
| A. Konstantinidis, G. Chatzimilioudis, D. Zeinalipour-Yazti, P. Mpeis, N. Pelekis, Y. Theodoridis: “Privacy-Preserving Indoor Localization on Smartphones”, IEEE Transactions on Knowledge and Data Engineering, 27(11): 3042-3055, November 2015. IEEE CS Press. |
| N. Pelekis, A. Gkoulalas-Divanis, M. Vodas, A. Plemenos, D. Kopanaki, Y. Theodoridis. Private-HERMES: a benchmark framework for privacy-preserving mobility data querying and mining methods. Proc. 15th International Conference on Extending Database Technology, (EDBT’12), Berlin, Germany, March 2012. |
| N. Pelekis, A. Gkoulalas-Divanis, M. Vodas, D. Kopanaki, Y. Theodoridis. Privacy-aware querying over sensitive trajectory data. Proc. 20th ACM International Conference on Information and Knowledge Management, (CIKM’11), Glasgow, Scotland, UK, October 2011. |
| M. Halkidi, I. Koutsopoulos. ‘A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems’, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2011. |
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