Prof. Jayanth R. Varma's Financial Markets Blog

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Prof. Jayanth R. Varma's Financial Markets Blog, A Blog on Financial Markets and Their Regulation

© Prof. Jayanth R. Varma
jrvarma@iima.ac.in

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Fri, 20 Oct 2017

Building credit bureaus that have no personal information

In two blog posts (here and here), I have argued that in an era of widespread hacking, the credit bureau’s business model is unsustainable because it requires storing enormous amounts of confidential information on tens of millions of individuals who are not even its customers.

However, these bureaus serve a useful function of aggregating information about an individual from multiple sources and condensing all this information into a credit score that measures the credit worthiness of the individual, An individual has credit relationships with many banks and other agencies. He might have a credit card from one bank, a car loan from another bank and a home loan from a third; he may have overdue payments on one or more of these loans. He might also have an unpaid utility bill. When he applies for a new loan from a yet another bank, the new bank would like to have all this information before deciding on granting the loan, but it is obviously impractical to write to every bank in the country to seek this information. It is far easier for all banks to provide information about all their customers to a central credit bureau which consolidates all this information into a composite credit score which can be accessed by any bank while granting a new loan.

The problem is that though this model is very efficient, it creates a single point of failure – a single entity that knows too much information about too many individuals. What is worse, these individuals are not customers of the bureau and cannot stop doing business with it if they do not like the privacy and security practices of the bureau.

We need to find ways to let the bureaus perform their credit scoring function without receiving storing confidential information at all. The tool required to do this (homomorphic encryption) has been available for over a decade now, but has been under utilized in finance as I discussed in a blog post two years ago.

Suppose there is only one bank

To explain how a secure credit bureau can be built, I begin with a simple example where the bureau obtains information only from one bank (or other agency) which has the individual as a customer. I will then extend this to multiple banks.

Extension to Multiple Banks

In general, the credit bureau will need information from many (say m) banks (or other agencies).

Allowing the individual to verify all computations

How does an individual detect any errors in the credit score? How does an external auditor verify the computations for a sample of individuals?

The individual k would be entitled to receive a credit report from the credit bureau that includes (a) the unencrypted total credit score (total_scorek), (b) the encrypted disguised_subscorekj for all j, (c) the encrypted modified weights vji for all i and j and (d) sum_rk. Actually, (b), (c) and (d) should be publicly revealed by the credit bureau on its website because they do not leak any information.

The individual k would also be entitled to get two pieces of information from bank j: (a) the attributes xjki for all i and (b) the random number rkj.

With this information, the individual k can verify the computation of the encrypted disguised_subscorekj for all j (using the same homomorphic encryption method used by the banks). The individual can also verify sum_rk by adding up the rkj. Using the public key of the credit bureau, the individual can also encrypt total_scorek - sum_rk and compare this with the encrypted sum obtained by adding up all the disguised_subscorekj homomorphically.

The same procedure would allow an auditor to verify the computation for any sample of individuals.

The careful reader might wonder how the individual can detect an attempt by a bank to falsify rkj. In that case, sum_rk will not match the sum obtained by adding up the rkj, but how can the individual determine which bank is at fault? To alleviate this problem, each bank j would be required to construct a Merkle tree of the rkj (for all k) and publicly reveal the root hash of this Merkle tree. Individual k would then also be entitled to receive a path of hashes in the Merkle tree leading up to rkj. It is then impossible to falsify any of the rkj without falsifying the entire Merkle tree. Any reasonable audit procedure would detect a falsification of the entire Merkle tree. Depending on the setup, the auditor might also be able to audit (a sample of) the secure multi party computation of rkj directly by verifying a (sub) sample of the secret shares.

Conclusion

At the end, we would have built a secure credit bureau. A Equifax scale hacking of such a bureau would be of no concern to the public; it would be a loss only for the bureau itself. Mathematics gives us the tools required to do this. The question is whether we have the good sense and the will to use these tools. The principal obstacle might be that the credit bureau would have to earn its entire income by selling credit scores; it would not be able to sell personal information about the individual because it does not have that information. But this is a feature and not a bug.

Posted at 16:20 on Fri, 20 Oct 2017     View/Post Comments (0)     permanent link