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William R. Lorimer

Publications and source records attributed to William R. Lorimer.

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Double Blind Comparisons using Groups with Infeasible Inversion

Double Blind Comparison is a new cryptographic primitive that allows a user who is in possession of a ciphertext to determine if the corresponding plaintext is identical to the plaintext for a different ciphertext held by a different user, but only if both users co-operate. Neither user knows anything about the plaintexts corresponding to either ciphertext, and neither user learns anything about the plaintexts as a result of the comparison, other than whether the two plaintexts are identical. Neither user can determine whether the plaintexts are equal without the other user's co-operation. Double Blind Comparisons have potential application in Anonymous Credentials and the Database Aggregation Problem. This paper shows how Double Blind Comparisons can be implemented using a Strong Associative One-Way Function (SAOWF). Proof of security is given, making an additional assumption that the SAOWF is implemented on a Group with Infeasible Inversion (GII), whose existence was postulated by Hohenberger and Molnar.

cs.CR

Double Blind Comparisons: A New Approach to the Database Aggregation Problem

The Data Aggregation Problem occurs when a large collection of data takes on a higher security level than any of its individual component records. Traditional approaches of breaking up the data and restricting access on a "need to know" basis take away one of the great advantages of collecting the data in the first place. This paper introduces a new cryptographic primitive, Double Blind Comparisons, which allows two co-operating users, who each have an encrypted secret, to determine the equality or inequality of those two secrets, even though neither user can discover any information about what the secret is. This paper also introduces a new problem in bilinear groups, conjectured to be a hard problem. Assuming this conjecture, it is shown that neither user can discover any information about whether the secrets are equal, without the other user's co-operation. We then look at how Double Blind Comparisons can be used to mitigate the Data Aggregation Problem. Finally, the paper concludes with some suggested possibilities for future research and some other potential uses for Double Blind Comparisons.

cs.CR