Surpriv: RFID Surveillance and Privacy

There is a call for papers out for a workshop to be held in conjunction with the International Conference on Emerging Trends in Information and Communication Security (ETRICS’06), June 6-9, 2006, Freiburg, German, entitled, “2006 Workshop on UC and RFID Today – Breakthrough or Still on Hold?” http://www.etrics.org/workshop_uart.php

CNET covers a GAO report on RFID use by Federal agencies, which suggests that they may be inattentive to security issues:

In a report published Friday, the Government Accountability Office said that 13 of the largest federal agencies are already using RFID or plan to use it. But only one of 23 agencies polled by the GAO had identified any legal or privacy issues–even though three admitted RFID would let them track employee movements.

Using SITIS, I posted a query on the DOD SBIR solicitation on “tracking important assets” noted previously; given the interest in identifying/tracking both “friendlies” and “unfriendlies” (which should presumably be RFID-tag-free, if they know what’s good for them!) RFID seems to be ruled out. The solicitation does seem a bit tilted toward magical solutions, though…

The SITIS page for the solicitation is here.

Knowledge@Wharton describes research on shopper behavior, collected via RFID tags on shopping carts:

In a new paper called “An Exploratory Look at Supermarket Shopping Paths,” Fader, Wharton marketing professor Eric T. Bradlow and doctoral candidate Jeffrey S. Larson analyze this RFID-captured grocery store data, focusing exclusively on travel patterns without regard to purchase behavior or merchandising tactics. The results, they conclude, challenge many long-standing perceptions of shopper travel behavior within a supermarket, including ideas related to aisle traffic, special promotional displays, and perimeter shopping patterns.

Using a new “multivariate clustering algorithm,” the authors identified 14 distinct grocery store travel paths during short, medium and long shopping trips…

Later in the article, there’s an interesting allusion to more fine-grained surveillance:

Until researchers are able to obtain positioning data directly from the shoppers themselves, Fader argues that PathTracker offers the next best thing — using customers’ grocery carts as a proxy for their shopping path…

Not clear either from the article that a tagged shopper would add too much to what’s being collected from tagged carts; even knowing where an individual was standing would give little information, unless one could also tell which direction s/he was looking, and especially if an item was selected. I suppose it would be possible to couple cart ID with POS purchase data, to retroactively label all the points where an item would have been selected and placed in the cart.

What’s really needed is a means to identify who the shoppers are who get a couple of pounds of deli meat, and stash it on a shelf in the cereal aisle… what’s up with that?

This is a reasonably interesting application of RFID for collection of personal data in a reasonably nonthreatening way, though it’s also possible to do this sort of data collection via video (e.g., using in-store cameras, with the shoppers’ trajectories calculated through use of machine vision).

A topic from the latest Department of Defense Small Business Innovation Research (SBIR) solicitation, DOD FY05.2, for the Army’s Special Operations Command (SOCOM), entitled, “Identify and Track Important Assets:”

SOCOM05-007 TITLE: Identify and Track Important Assets

TECHNOLOGY AREAS: Information Systems, Sensors, Electronics

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), which controls the export and import of defense-related material and services. Offerors must disclose any proposed use of foreign nationals, their country of origin, and what tasks each would accomplish in the statement of work in accordance with section 3.5.b.(7) of the solicitation.

OBJECTIVE: The development of an innovative, inexpensive device that provides the ability to identify important assets using light weight, low power, man portable technology without exposing the monitoring position or the person/device monitoring the intended target.

DESCRIPTION: Identification and tracking of important assets would be very valuable for military, law enforcement and commercial applications. While Industry is predominately focused on identification at close range e.g. inventory control systems, a need exist to identify important assets at range. Application of this technology could include military and law enforcement surveillance activities, large-scale commercial control systems, as well as security for retail stores and the gaming industry. Current technology focus areas include RFID, barcode scanning, fingerprinting and facial recognition to name a few. These technologies require relatively close proximity to the asset as well as a baseline database for comparison. RFID and facial recognition technologies show the most promise for large-scale stand-off identification of important assets but each has drawbacks and limitations. RFID technologies require tagging of assets and may not be practical in all scenarios, also power requirements increase with range. Facial recognition proves problematic in low light conditions and requires further algorithm development and increased computing power. Any new technology should satisfy the following requirements:

� The asset should not be manipulated during the identification or monitoring process
� Identification should be feasible in all light/environmental conditions
� Any method used for identification must be visually undetectable
� The identification must occur without physical contact with the individual to be monitored
� Should be ruggedized and have a small, lightweight form factor
� Should utilize Commercial Off-The-Shelf equipment to minimize follow-on production costs
� Should be low power with the ability to use AC or DC power
� Should be designed with a network environment in mind to allow for unattended operation

Development of such a capability would satisfy aspects of Sensors, Electronics and Electronic Warfare a DoD Critical Technology Area.

PHASE I: Develop a proof of concept for the proposed method of identifying individuals at range.

PHASE II: Develop a working prototype that can be tested by end users in a real life working environment that resembles the form factor of the final system.

PHASE III DUAL-USE APPLICATIONS: Produce operation units that can be employed by the military, law enforcement and the commercial market sector.

KEYWORDS: sensors, tagging, tracking, electronics

TPOC: Mr. Dave Saren
Phone: (813) 828-9363
Fax: (813) 828-9429
Email: sarend@socom.mil

NB that it doesn’t necessarily specify that what’s to be identified is “non-cooperative,” i.e., one could imagine this is just to keep tabs on “friendlies,” but it doesn’t preclude this being applied to surveillance, either. In a nutshell, it says, “Technologies like RFID and facial recognition are useful, but it’d be nice if they could be useful from even greater distances.” NB also that this is primarily concerned with the RF end of things (the part that’s less interesting to me than the back-end, information aggregation and analysis).

The abstract of an NSF award, for “Network Analysis Using Inverse Optimization”:

This research is aimed at using mathematical optimization to solve a wide range of problems, including improving internet routing protocols, devising efficient methods of pricing telecommunications bandwidth, and inferring traffic flows in real-time from anonymous cell phone data. The most common intra-domain internet routing protocol requires creating fictitious “costs” for each link in the network. The standard heuristic method for doing that may lead to unnecessary network congestion. Observations of the bandwidth market show that there is often inefficiency in pricing, which allows arbitrage: clever purchasers can often construct more valuable connectivity than they pay for. A National Highway Traffic Safety Administration study estimated that 3% of American drivers are talking on cell phones at any given time; by anonymously tracking the progression of each call from one cell tower to another, it may be possible to estimate traffic patterns in the area without violating any individual caller’s privacy. Although seemingly unrelated, these problems all have similar underlying mathematical structure. The shared mathematical structure of these optimization problems will be investigated and the knowledge gained will be used to develop ways of obtaining better solutions to each of these problems, as well as others which share the same properties. Practical outcomes include a decrease in network congestion, increased efficiency in the bandwidth marketplace, and real-time methods for deducing and responding to roadway congestion.

But all those data, so tempting, tempting… we ought to presume that all of this transactional information captured by service providers and others may well be at risk of compromise, abuse, or wholesale requisition (a la air travel information provided to the government by airlines). How to design systems that are more defensive of privacy?