Fingerprinting of RFID Tags and High-Tech Stalking
Previous articles in this series looked at fingerprinting of blank paper and digital cameras. This article is about fingerprinting of RFID, a domain where research has directly investigated the privacy threat, namely tracking people in public.
The principle behind RFID fingerprinting is the same as with digital cameras:
The basics. First let’s get the obvious question out of the way: why are we talking about devious methods of identifying RFID chips, when the primary raison d’être of RFID is to enable unique identification? Why not just use them in the normal way?
The answer is that fingerprinting, which exploits the physical properties of RFID chips rather than their logical behavior, allows identifying them in unintended ways and in unintended contexts, and this is powerful. RFID applications, for example in e-passports or smart cards, can often be cloned at the logical level, either because there is no authentication or because authentication is broken. Fingerprinting can make the system (more) secure, since fingerprints arise from microscopic randomness and there is no known way to create a tag with a given fingerprint.
If sensor patterns in digital cameras are a relatively clean example of fingerprinting, RF (and anything to do with the electromagnetic spectrum in general) is the opposite. First, the data is an arbitrary waveform instead of an fixed-size sequence of bits. This means that a simple point-by-point comparison won’t work for fingerprint verification; the task is conceptually more similar to algorithmically comparing two faces. Second, the probe signal itself is variable. RFID chips are passive: they respond to the signal produced by the reader (and draw power from it). This means that the fingerprinting system is in full control of what kind of signal to interrogate the chip with. It’s a bit like being given a blank canvas to paint on.
Techniques. A group at ETH Zurich has done some impressive work in this area. In their 2009 paper, they report being able to compare an RFID card with a stored fingerprint and determine if they are the same, with an error rate of 2.5%–4.5% depending on settings. They use two types of signals to probe the chip with — “burst” and “sweep” — and extract features from the response based on the spectrum.
Other papers have demonstrated different ways to generate signals/extract features. A University of Arkansas team exploited the minimum power required to get a response from the tag at various frequencies. The authors achieved a 94% true-positive rate using 50 identical tags, with only a 0.1% false-positive rate. (About 6% of the time, the algorithm didn’t produce an output.)
Yet other techniques, namely the energy and Q factor of higher harmonics were studied in a couple of papers out of NIST. In the latter work, they experimented with 20 cards which consisted of 4 batches of 5 ‘identical’ cards in each. The overall identification accuracy was 96%.
It seems safe to say that RFID fingerprinting techniques are still in their infancy, and there is much room for improvement by considering new categories of features, by combining different types of features, or by using different classification algorithms on the extracted features.
Privacy. RF fingerprinting, like other types of fingerprinting, shows a duality between security-enhancing and privacy-infringing applications, but in a less direct way. There are two types of RFID systems: “near-field” based on inductive coupling, used in contactless smartcards and the like, and “far field” based on backscatter, used in vehicle identification, inventory control, etc. The papers discussed so far pertain to near-field systems. There are no real privacy-infringing applications of near-field RF fingerprinting, because you can’t get close enough to extract a fingerprint without the owner of the tag knowing about it. Far-field systems, to which we will now turn, are ideally suited to high-tech stalking.
In a recent paper, the Zurich team mentioned earlier investigated the possibility of tracking a people in a shopping mall based on strategically placed sensors, assuming that shoppers have several (far-field) RFID tags on them. The point is that it is possible to design chips that prevent tracking at the logical level by authenticating the reader, but this is impossible at the physical level.
Why would people have RFID tags on them? Tags used for inventory control in stores, and not deactivated at the point-of-sale are one increasingly common possibility — they would end up in shopping bags (or even on clothes being worn, although that’s less likely). RFID tags in wallets and medical devices are another source; these are tags that the user wants to be present and functional.
What makes the tracking device the authors built powerful is that it is low-cost and can be operated surreptitiously at some distance from the victim: up to 2.75 meters, or 9 feet. They show that 5.4 bits of entropy can be extracted from a single tag, which means that 5 tags on a person gives 22 bits, easily enough to distinguish everyone who might be in a particular mall.
To assess the practical privacy risk, technological feasibility is only one dimension. We also need to ask who the adversary is and what the incentives are. Tracking people, especially shoppers, in physical space has the strongest incentive of all: selling products. While online tracking is pervasive, the majority of shopping dollars are still spent offline, and there’s still no good way to automatically identify people when they are in the vicinity in order to target offers to them. Facial recognition technology is highly error-prone and creeps people out, and that’s where RF fingerprinting comes in.
That said, RF fingerprinting is only one of the many ways of passively tracking people en masse in physical space — unintentional leaks of identifiers from smartphones and logical-layer identification of RFID tags seem more likely — but it’s probably the hardest to defend against. It is possible to disable RFID tags, but this is usually irreversible and it’s difficult to be sure you haven’t missed any. RFID jammers are another option but they are far from easy to use and are probably illegal in the U.S. One of the ETH Zurich researchers suggests tinfoil wrapping when going out shopping :-)
 Active RFID chips exist but most commercial systems use passive ones, and that’s what the fingerprinting research has focused on.
 They used a population of 50 tags, but this number is largely irrelevant since the experiment was one of binary classification rather than 1-out-of-n identification.
Thanks to Vincent Toubiana for comments on a draft.