Xerox Corporation

Johan Clier

Ioan Calapodescu

Nicolas Monet

Thierry Jacquin

Saghar Estehghari

A method for data matching includes providing two sets of encrypted data elements by converting data elements to respective sets of vectors and encrypting each vector with a public key of a homomorphic encryption scheme. Each data element includes a sequence of characters drawn from an alphabet. For pairs of encrypted data elements, a comparison measure is computed between the sets of encrypted vectors. An obfuscated vector is generated for each encrypted data element in the first set, which renders the first encrypted data element indecipherable when the comparison measure does not meet a threshold for at least one of the pairs of data encrypted elements comprising that encrypted data element. The obfuscated vectors can be decrypted with a private key, allowing data elements in the first set to be deciphered if the comparison measure meets the threshold for at least one of the data elements in the second set.

Compact fuzzy private matching using a fully-homomorphic encryption scheme

Data privacy is becoming increasingly important and, in some jurisdictions, required. Access to private data can be controlled by forcing all access to go through minimizations services that allow only authorized access to private data. These minimization services can become processing bottlenecks if the only way to modify private data is by way of requests to the minimization service. Certain homomorphic operations allow for encrypted data to be modified without being first decrypted although other operands must be encrypted. Augmenting a minimization service to provide a public encryption key provides for encryption of the other operands. Providing a records manager that can take advantage of homomorphic operations allows certain data operations to be performed without compromising security and without accessing the minimization service.

Homomorphic cryptography modeling in support of privacy policies

A system and method for text language identification allow private information of a server and a client to be kept secret from each other. An encrypted score for each of a plurality of languages is received by the server from the client. The encrypted scores are generated by homomorphic addition of encrypted frequencies of n-grams in a list of n-grams extracted from text. The unencrypted list is not provided to the server. The encrypted frequencies of the n-grams in the list are extracted using encrypted resources which, for each of the plurality of languages, include an encrypted frequency for each of a set of n-grams. At the server, the encrypted scores are decrypted to generate unencrypted scores and information is provided to the client based on the unencrypted scores from which the client is able to identify a language for the text.

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Meylan, France

Johan Clier