PETS in real time: Location privacy
27 July 2011
Quantifying Location Privacy: The Case of Sporadic Location Exposure
Reza Shokri and George Theodorakopoulos (EPFL), George Danezis (Microsoft Research), and Jean-Pierre Hubaux and Jean-Yves Le Boudec (EPFL)
This work evaluates the privacy of using location-based services sporadically using a set of location privacy mechanisms. Sporadic services include those that require location infrequently, rather than continuously (think of restaurant suggestions rather than relaying real-time GPS streams). The key novelty of the approach is that the model of location exposure, as well as privacy protection is very general. It encompasses anonymization, generalization and obfuscation of location, use of fake traffic and suppression of location. In turn the analysis relies on advanced models of location and mobility (based on markov chains) and is based on Bayesian inference. The evaluation of different location privacy techniques is done on real-world traces from SF taxis.
I am one of the authors of this work, so of course I think it is awesome! More seriously, it is one of the fist works to combine under a common framework a multitude of location privacy mechanisms, and provide a common evaluation framework for them, to quantify the degree of protection they offer relatively to each other for different adversaries. It is also one of the first systematic applications of Bayesian inference to analyze location privacy — extending the inference paradigm beyond the analysis of network anonymity systems.
Of course this is not the last word. Only a subset of protection techniques and combination of techniques were look at, and other protection mechanisms can be integrated and evaluated in the same framework (the tracing model and threat model can be unchanged). Secondly, the analysis itself may be augmented with side-information — be it commercial transactions or traces of network traffic — that may be giving some information about location, to increase the capabilities of the adversary (or make them more realistic). The model we use, based on markov chains, has the benefit of giving analytically tractable results, but numerical techniques may be used to extend it to be more true to real-life attacks.
Privacy in Mobile Computing for Location-Sharing-Based Services
Igor Bilogrevic and Murtuza Jadliwala (EPFL), Kubra Kalkan (Sabanci University), Jean-Pierre Hubaux (EPFL), and Imad Aad (Nokia)
This paper looks at applications where users need to share their location. For example, two users may want to find out if they are close to each other or where they should meet in order to share a taxi ride. Yet, those users do not want to leak any of their location information to the other users or the service provider. More specifically two users specify a set of ranked prefered location they could meet and the system needs to determine on of those fairly without revealing the current location or other preferences (except the one chosen to meet). This is called the fair rendez-vous problem.
The key contribution of this work is to show that this problem can be set with a set of concrete cryptographic protocols. It also presents an implementation of these algorithms on a real mobile phone to show that it is practical. The cryptographic computations are based on homomorphic encryption schemes as well as interactions with the service (to do multiplication that is not possible with Paillier). The implementation on a mobile phone takes a few seconds on the client and the server, and is paralelizable in the number of users. Untypically, the authors also did a user study: users were asked what their concerns were, and after using the application of the phone they were asked how usable it was, and whether they appriciated the privacy provided by the application.
This is a really nice example of a privacy application, that applies advanced crypto, but also evaluates it on a real platform for performance as well as users’ reaction to it. The obvious extensions to this work would be to generalize it to more complex rendez-vous protocols, as well as other location sharing applications. It is good to see that modern mobile devices can do plenty of crypto in a few seconds, so I am very hopeful we will see more work in this field.
On The Practicality of UHF RFID Fingerprinting: How Real is the RFID Tracking Problem?
Davide Zanetti, Pascal Sachs, and Srdjan Capkun (ETH Zurich)
This paper looks that UHF tags — they are the dumb tags that can be read at about 2m that are attached to things you buy to facilitate stock management or customer aftercare. Interestingly this study looks at how identifiable the tags are at the physical layer, not using the actual tag ID! Therefore these techniques may bypass any privacy protection that attempt to prevent access to the tag ID. It turns our that one can build a unique and reliable ID for a tag from its physical characteristics that can be used to trace people as they move around.
What is new about this work is that the focus was on practicality and cost of extracting a reliable fingerprint (previous approaches relied on expensive equipment and laboratory conditions). The solution was implemented using a cheap software radio (USRP2 device + PC).
I am not quite sure what to conclude from the evaluation on the quality of the fingerprint. It seems that an adversary can place tags within one of 83 to 100 groups. Is this really a good results or not? I guess it depends on the application and the density of tags. Of course if more than one tag is carried, then the adversary could combine fingerprints to identify individuals more easily — if you carry 5 tags you have a 20 bit IDs. Interestingly, there is extensive evaluation of the stability of the tag to temperature and mobility — it turns out that these factors do affect the quality of the fingerprint and further reduce the effective number of unique IDs that can be extracted (down to about 49 classes).
It would be interesting to combine this attack vector with the ideas from the first paper (pretending that the short physical IDs are a version of a privacy protection system) to evaluate the effectivness of tracing a set of individual throughout town.