PRIVACY-PRESERVING RIDGE REGRESSION
A hybrid approach to privacy-preserving ridge regression is presented that uses both homomorphic encryption and Yao garbled circuits. Users in the system submit their data encrypted under a linearly homomorphic encryption. The linear homomorphism is used to carry out the first phase of the algorithm that requires only linear operations. The output of this phase generates encrypted data, in a form that is independent of the number of users n. In a second phase, a Yao garbled circuit that first implements homomorphic decryption and then does the rest of the regression algorithm (as shown, an optimized realization can avoid decryption in the garbled circuit) is evaluated. For this step a Yao garbled circuit approach is much faster than current fully homomorphic encryption schemes. Thus the best of both worlds is obtained by using linear homomorphisms to handle a large data set and using garbled circuits for the heavy non-linear part of the computation.
1 . A method for providing privacy-preserving ridge regression, the method comprising:
requesting a garbled circuit from a crypto service provider;
collecting data from multiple users that has been formatted and encrypted using homomorphic encryption;
summing the data that has been formatted and encrypted using homomorphic encryption; and
evaluating the garbled circuit from the crypto service provider with the summed data using oblivious transfer.
2 . The method of claim 1 , wherein the step of requesting a garbled circuit from a crypto service provider comprises:
providing a dimension of the input variables for the garbled circuit; and
providing the value range of the input variables.
3 . The method of claim 1 wherein an evaluator implemented on a computing device performs the method.
4 . The method of claim 3 wherein the crypto service provider is implemented on a computing device remote from the computing device the evaluator is implemented on.
5 . The method of claim 1 further comprising the step of providing an encryption key for encrypting the data from multiple users.
6 . The method of claim 5 wherein the data from multiple users is further encrypted with an encryption key provided by the crypto service provider.
7 . The method of claim 1 wherein the step of evaluating the garbled circuit further comprises:
decrypting the summed data; and
solving the ridge regression equation embodied by the garbled circuit.
8 . The method of claim 1 wherein the step of collecting data from multiple users comprises receiving data sent from each of the multiple users via a computing device.
9 . A computing device for providing privacy-preserving ridge regression, the computer device comprising:
a storage for storing user data;
a memory for storing data for processing; and
a processor configured to request a garbled circuit from a crypto service provider, collect data from multiple users that has been formatted and encrypted using homomorphic encryption, sum the data that has been formatted and encrypted using homomorphic encryption, and evaluate the garbled circuit from the crypto service provider with the summed data using oblivious transfer.
10 . The computing device of claim 9 further comprising a network connection for connecting to a network.
11 . The computing device of claim 9 wherein the crypto service provider is implemented on a separate computing device.
12 . The computing device of claim 9 wherein the step of requesting a garbled circuit from a crypto service provider comprises:
providing a dimension of the input variables for the garbled circuit; and
providing the value range of the input variables.
13 . The computing device of claim 9 wherein the step of evaluating the garbled circuit further comprises:
decrypting the summed data; and
solving the ridge regression equation embodied by the garbled circuit.
14 . The computing device of claim 9 , wherein the data from multiple users is encrypted with an encryption key provided by the crypto service provider and encrypted with and encryption key by the computing device.
15 . A machine readable medium containing instructions that when executed perform the steps comprising:
requesting a garbled circuit from a crypto service provider;
collecting data from multiple users that has been formatted and encrypted using homomorphic encryption;
summing the data that has been formatted and encrypted using homomorphic encryption; and
evaluating the garbled circuit from the crypto service provider with the summed data using oblivious transfer.