Despite India’s economic boom, more than a third of the country remains impoverished, with 456 million people subsisting on less than $1.25 per day, according to the most recent World Bank figures. Government subsidies on everything from food to fuel have tried to spread the nation’s wealth, but rampant corruption has made the redistribution pipeline woefully inefficient.
The “leakage” happens in part because the benefits aren’t directed at specific individuals, says Salil Prabhakar, a Silicon Valley-based computer scientist who is working as a volunteer for the World Bank as part of the Unique ID (UID) project, a massive biometrics initiative aimed at overhauling the current system. “If I as the government issue $1 of a benefit,” Prabhakar explains, “I don’t know where it’s going. I just know that there’s a poor person in some remote village, and I hope it reaches them.”
The current system relies on a chain of middlemen—many of them corrupt bureaucrats at various levels of government—who collectively siphon off 10% or more of what’s due to the poor and resell the goods and services on the black market. For example, according to Transparency International, officials extracted $212 million in bribes alone from Indian households below the poverty line in 2007.
The UID project—helmed by the much-admired former Infosys Technologies CEO Nandan Nilekani and operated by Unique Identification Authority of India, a government agency—promises to be the first step in the solution. Recently renamed Aadhaar (meaning “foundation” in Hindi), the UID project plans to assign a unique 16-digit number to each citizen above the age of 18 who wants national identification, and to link that number with the owner’s biometric data—all 10 fingerprints, an iris scan, and a headshot (plus four hidden “virtual” digits). Aadhaar’s national enrollment was launched in September, with the goal of issuing 100 million ID numbers by March and 600 million within four years. Like the Social Security number in the U.S., the number won’t guarantee government aid, but your biometrics will prove the UID is yours, letting you claim whatever benefits to which you’re entitled. In theory, the result should be the end of counterfeit ration cards and other fraud, as well as making it easier for hundreds of millions of Indian adults to gain easier access to banking services for the first time. And because the system will work nationwide, Aadhaar should make it possible for the poor to move without losing benefits. The lower-income Indians love the idea, says Prabhakar, who witnessed what he describes as “almost a stampede” during a recent proof-of-concept enrollment.
The full implementation, though, is fraught with problems, most of which stem from the project’s sheer size, given India’s population of 1.2 billion. “Biometric systems have never operated on such a massive scale,” says Arun Ross, an associate professor of computer science at West Virginia University.
One of the biggest challenges is deduplication. When a new user tries to enroll, the system must check for duplicates by comparing the new user’s data against all the other records in the UID database. Hundreds of millions of records make this a computationally demanding process, made all the more so by the size of each record, which includes up to 12 higher-resolution images.
The demands continue each time there’s an authentication request. “The matching is extremely computationally intensive,” says Prabhakar. At peak times, the system must process tens of millions of requests per hour while responding in real time, requiring massive data centers the likes of Google’s.
Achieving acceptable levels of accuracy at this scale is another major difficulty. Unlike passwords, biometrics never produce an exact match, so matching always entails the chance of false accepts and false rejects, but as the number of enrollments rises, so do the error rates, since it becomes more likely that two different individuals will share similar biometrics. Using a combination of biometrics—instead of a single thumbprint, for example—greatly improves accuracy and deters impostors. (In the words of Marios Savvides, assistant research professor in the department of electrical and computer engineering at Carnegie Mellon University, “It’s hard to spoof fingerprints, face, and iris all at the same time.”) But using multiple biometrics requires extra equipment, demands information fusion, and adds to the data processing load.
Other steps to improve accuracy also bring their own challenges. “The key issue,” says Nalini Ratha, a researcher at the IBM Watson Research Center, “is have I captured enough variation so I don’t reject you, and at the same time I don’t match against everybody else?” Capturing the optimal amount of variation requires consistent conditions across devices in different settings—no easy feat in a country whose environment varies from deserts to tropics and from urban slums to far-flung rural areas. “It’s almost like having many different countries in a single country, biometrically speaking,” says Ross.
The challenge isn’t just to reduce errors—under some conditions, a biometric reader may not work at all. “If it’s too hot, people sweat and you end up with sweaty fingers,” says Prabhakar, “and if it’s too dry, the finger is too dry to make good contact with the optical surface of the scanner.” Normalizing across varied lighting conditions is essential, since all of the biometric data is optical.
India’s diverse population presents a whole other set of hurdles. Many of the poor work with their hands, but manual labor leads to fingertips so callused or dirty they can’t produce usable finger-prints. And some of the most unfortunate residents are missing hands or eyes altogether.
Security Challenges
As if these problems weren’t enough, the UID system poses formidable security challenges beyond the threat of spoofing. “People get carried away by one type of attack—a fake finger, a fake mask, or something,” says IBM’s Ratha, “but there are probably 10 other attacks to a biometric system that can compromise the system.”
“The key issue,” says Nalini Ratha, “is have I captured enough variation so I don’t reject you, and at the same time I don’t match against everybody else?”
For starters, when data is stored in a centralized database, it becomes an attractive target for hackers. Another vulnerability is the project’s reliance on a network of public and private “registrars”—such as banks, telecoms, and government agencies—to collect biometric data and issue UIDs. Though registrars might ease enrollment, they’re not necessarily worthy of the government’s trust. Banks, for example, have been helping wealthy depositors evade taxes by opening fictitious accounts, so entrusting the banks with biometric devices doesn’t make sense, says Sunil Abraham, executive director of the Centre for Internet and Society in Bangalore. “If I’m a bank manager, I can hack into the biometric device and introduce a variation in the fingerprint because the device is in my bank and the biometric is, once it’s in the computer, just an image sent up the pipe,” he says. Though careful monitoring could catch such hacks, Abraham says that’s not realistic once you’ve got as many records as Aadhaar will have.
Registrars may also make UIDs, which are officially voluntary, a de facto requirement for services, especially in the current absence of a law governing how the data can be used. Such “function creep” troubles privacy advocates like Malavika Jayaram, a partner in the Bangalore-based law firm Jayaram & Jayaram, who says, “If every utility and every service I want is denied to me without a UID card, how is it voluntary?” The loss of civil liberties is too high a price to pay for a system that she believes leaves gaping opportunities for continued corruption. “The guy handing out the bags of rice could ask for a bribe even to operate the machine that scans the fingerprints, or he could say that the machine isn’t working,” says Jayaram. “And there’s every chance the machine isn’t working. Or he could say, ‘I don’t know who you are and I don’t care; just pay me 500 rupees and I’ll give you a bag of rice.’ All the ways that humans can subvert the system are not helped by this scheme.”
Abraham suggests a more effective way to root out fraud through biometrics would be to target the much smaller number of residents who own most of the country’s wealth, much of it illgotten. “The leakage is not happening at the bottom of the pyramid,” he says. “It’s bureaucrats and vendors and politicians throughout the chain that are corrupt.”
Despite all the technical and social challenges, Nandan Nilekani’s UID project is on course to provide 100 million Indian residents with a Unique ID by March. Will Nilekani’s UID scheme work? Only time will tell. “But if there’s anybody in India who’s capable of pulling it off, it’s him,” says Abraham. Meanwhile, the hopes of millions of India’s poor are invariably tied to the project’s success.
Further Reading
Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.
Guide to Biometrics. Springer, New York, NY, 2004.
Jain, A.K., Flynn, P. and Ross, A. (Eds.)
Handbook of Biometrics. Springer, New York, NY, 2007.
Pato, J.N and Millett, L.I. (Eds.)
Biometric Recognition: Challenges and Opportunities. The National Academies Press, Washington, D.C., 2010.
Ramakumar, R.
High-cost, high-risk, FRONTLINE 26, 16, Aug. 114, 2009.
Ross, A. and Jain, A.K.
Information fusion in biometrics, Pattern Recognition Letters 24, 13, Sept. 2003.
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