US 12,143,465 B2
Searching over encrypted model and encrypted data using secure single-and multi-party learning based on encrypted data
Karthik Nandakumar, Singapore (SG); Nalini K. Ratha, Yorktown Heights, NY (US); Shai Halevi, Elmsford, NY (US); and Sharathchandra Pankanti, Darien, CT (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on May 17, 2019, as Appl. No. 16/415,125.
Prior Publication US 2020/0366459 A1, Nov. 19, 2020
Int. Cl. H04L 9/00 (2022.01); G06F 21/62 (2013.01); G06N 20/00 (2019.01)
CPC H04L 9/008 (2013.01) [G06F 21/6245 (2013.01); G06N 20/00 (2019.01); H04L 2209/46 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method, comprising:
accessing, by a computer system, training data curated to include information prohibited from release;
accessing one or more machine learning models that, when applied to the training data, releases the information that is prohibited, wherein the one or more machine learning models comprise N machine learning models, where N is at least 1, and wherein the method further comprises testing the N machine learning models at least by,
directing, by the computer system, one or more users to encrypt at least the training data, to form encrypted training data, in individual fully homomorphic encryption (FHE) domains corresponding to the one or more users;
running by the computer system the N machine learning models on the encrypted training data, the running determining first encrypted results;
comparing, by the computer system using a metric, the first encrypted results with second encrypted results of a further machine learning model, different from the N machine learning models, run on at least the encrypted training data, wherein the comparing the first encrypted results with the second encrypted results further comprises determining whether the further machine learning model is performing prohibited release of the information; and
taking one or more actions to address the prohibited release of the information based on the determination that the further machine learning model is performing the prohibited release of the information.