Titulo Estágio
Location Privacy for Smartphones
Áreas de especialidade
Comunicações, Serviços e Infraestruturas
Engenharia de Software
Local do Estágio
DEI-FCTUC
Enquadramento
Pervasive and always connected mobile devices such as smartphones have led to ubiquitous data collection. While this has proved beneficial to provide tailored and customized services to users, serious privacy concerns arise from the collection, analysis and use of this sensitive private data [2]. For example, user information can be used for mechanisms such as behavioral pricing, in which prices are adjusted according to user behavior, many times not for benefit of individuals.
These privacy risks are particularly relevant in location-based services (LBS) on mobile devices. It is now well known that the unique identification of users is feasible with a small number (4) of collected location points, even within a dataset of one and a half million individuals [1].
To address this issue, some works consider application of classical data transformation techniques such as noise addition, generalization and suppression [2] of data to obfuscate the original data. For example, instead of providing exact location, provide an approximate area for the user. A major challenge here lies in developing methodologies that are able to protect users against not only a single time instance, but over time against continuous collection of location data, which enables the aforementioned unique identification of users.
Objetivo
The goal of this thesis is to develop mechanisms for privacy-protection of location information in mobile devices. These mechanisms shall protect the user from the constant collection of location information that is already happening and is foreseeable to increase in upcoming years. In particular, these privacy-protection mechanisms will rely on classical privacy-preserving techniques such as noise addition, generalization and suppression with the goal of reducing the likelihood of unique identification of users. This will require, that these techniques are adapted to take into consideration that users must be protected against not only a single time instance, but over time against continuous collection of location data. Moreover, it is also important to keep an appropriate balance between utility and privacy of location data, such that this data is still useful to enable tailored services, without compromising privacy at large.
Plano de Trabalhos - Semestre 1
1) State-of-the-art study on:
- privacy-enabling mechanisms for location-based services in mobile devices;
- methods to compromise location privacy in mobile devices;
- measures/metrics of privacy and utility.
2) Intermediate report
Plano de Trabalhos - Semestre 2
1) Development of privacy-enhancing mechanisms for location privacy in mobile devices;
2) Implementation and evaluation of the privacy-enhancing mechanisms taking into consideration utility and privacy levels achieved;
3) Implementation of a prototype mobile application that incorporates the developed privacy techniques into modern smartphones (e.g. android);
4) Writing of master thesis and scientific article.
Condições
- A research scholarship will be provided.
- Familiarity with privacy-preserving techniques is essential.
Observações
Related news article: [url=https://mobile.nytimes.com/2017/01/20/technology/personaltech/how-your-phone-knows-where-you-have-been.html]https://mobile.nytimes.com/2017/01/20/technology/personaltech/how-your-phone-knows-where-you-have-been.html[/url]
References / related works:
[1] Yves-Alexandre De Montjoye, César A Hidalgo, Michel Verleysen, and Vincent D Blondel. Unique in the crowd: The privacy bounds of human mobility. Nature Scientific Reports, 3:1376, 2013. URL: [url=https://www.nature.com/articles/srep01376]https://www.nature.com/articles/srep01376[/url]
[2] Ricardo Mendes and João P. Vilela. Privacy-Preserving Data Mining: Methods, Metrics and Applications. IEEE Access (accepted for publication), 2017. URL: [url=https://eden.dei.uc.pt/~jpvilela/conpute/ppdm-IEEE_Access2017.pdf]https://eden.dei.uc.pt/~jpvilela/conpute/ppdm-IEEE_Access2017.pdf[/url]
[3] K. Olejnik, I. Dacosta, J.S. Machado, K. Huguenin, M.E. Khan, J.P. Hubaux, SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices, Proceedings of the 38th IEEE Symposium on Security and Privacy, May 2017. URL: [url=https://infoscience.epfl.ch/record/226751/files/Olejnik2017SP.pdf]https://infoscience.epfl.ch/record/226751/files/Olejnik2017SP.pdf[/url]
[4] J Krumm, A survey of computational location privacy, Springer Personal and Ubiquitous Computing, August 2009. URL: [url=https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/computational-location-privacy-preprint.pdf]https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/computational-location-privacy-preprint.pdf[/url]
Orientador
João Vilela
jpvilela@dei.uc.pt 📩