Anonymous encrypted data

• US 10,700,864 B2
• Filed: 07/12/2017
• Issued: 06/30/2020
• Est. Priority Date: 07/12/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Antonatos, Spyridon, Braghin, Stefano, Kaul, Akshar, Kesarwani, Manish, Mehta, Sameep
• Primary Examiner(s): Thiaw, Catherine
• Application Number:
  US15/648,179
• Publication Number:
  US 20190020475A1
• Time in Patent Office: 1,084 Days
• CPC Class Codes: G06F 12/1408 by using cryptography for digital transmission H04L9/00
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 21/64 Protecting data integrity, e.g. using checksums, certificates or signatures
• CPC Class Codes: G06F 2212/1052 Security improvement
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 63/0421 Anonymous communication, i.e. the party's identifiers are hidden from the other party or parties, e.g. using an anonymizer
• CPC Class Codes: H04L 63/0428 wherein the data content is protected, e.g. by encrypting or encapsulating the payload
• CPC Class Codes: H04L 63/107 wherein the security policies are location-dependent, e.g. entities privileges depend on current location or allowing specific operations only from locally connected terminals
• CPC Class Codes: H04L 9/0894 Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage
• CPC Class Codes: H04L 9/32 including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials

Abstract

Techniques facilitating autonomously rendering an encrypted data anonymous in a non-trusted environment are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm. The computer-implemented method can also comprise modifying, by the system, the plurality of clusters based on a defined criterion that can facilitate anonymity of the encrypted data.

Claims

1. A system, comprising:

a memory that stores computer executable components;

a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory, wherein the computer executable components comprise;

a clustering component that generates a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm, wherein the machine learning algorithm is a distance based clustering algorithm based on a location identifier of geographical coordinates;

a modifying component that modifies the plurality of clusters based on a defined security requirements that facilitates anonymity of the encrypted data, wherein the modification comprises re-assigning one or more members of a non-compliant cluster of the plurality of clusters to a nearest cluster with respect to the one or more members, and wherein the re-assigning the one or more members comprises;

sorting, by size, clusters of the plurality of clusters that fail to meet the defined security requirements, wherein the sorting is, sorting from a cluster with the fewest members to a cluster with the most members, the clusters that fail to meet the defined security requirements;

re-assigning members of the cluster with the fewest members that is a non-compliant cluster to the nearest cluster;

after the re-assigning, removing the cluster with the fewest members from the plurality of clusters and re-analyzing the plurality of clusters for other non-compliant clusters; and

performing the re-assigning the one or more members iteratively until all non-compliant clusters of the plurality of clusters have been removed; and

wherein the modification renders the encrypted data anonymous on a non-trusted environment.

2. The system of claim 1, wherein the clustering component and the modification component operate in a federated cloud environment.

3. The system of claim 1, wherein the clustering component further generates a cluster representative for a cluster from the plurality of clusters.

4. The system of claim 1, wherein the defined security requirements is a minimum number of encrypted data records per cluster.

5. The system of claim 4, wherein in response to a cluster having less than the defined criterion, the modifying component modifies the plurality of clusters using an operation selected from a group consisting of suppression of the cluster and re-assignment of the encrypted data of the cluster.

6. The system of claim 5, wherein the suppression of the cluster comprises removing a portion of the encrypted data from the plurality of clusters based on a suppression threshold.

7. A computer program product facilitating rendering an encrypted dataset anonymous, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

generate a plurality of clusters of encrypted data from the encrypted dataset using a machine learning algorithm, wherein the machine learning algorithm is a distance based clustering algorithm based on a location identifier of geographical coordinates;

modify the plurality of clusters based on a defined security requirements that facilitates anonymity of the encrypted data, wherein modification comprises re-assigning one or more members of a non-compliant cluster of the plurality of clusters to a nearest cluster with respect to the one or more members, and wherein the re-assigning the one or more members comprises;

a sorting, by size, of clusters of the plurality of clusters that fail to meet the defined security requirements, wherein the sorting is sorting from a cluster with the fewest members to a cluster with the most members, the clusters that fail to meet the defined security requirements;

a re-assigning of members of the cluster with the fewest members that is a non-compliant cluster to the nearest cluster;

after the re-assigning, removal of the cluster with the fewest members from the plurality of clusters and re-analysis of the plurality of clusters for other non-compliant clusters; and

performance of the re-assigning of the one or more members iteratively until all non-compliant clusters of the plurality of clusters have been removed; and

wherein the modification renders the encrypted data anonymous on a non-trusted environment.

8. The computer program product of claim 7, wherein the program instructions further cause the processor to encrypt a dataset via a plurality of encryption schemes to generate a plurality of encrypted datasets, and the encrypted dataset is from the plurality of encrypted datasets.

9. The computer program product of claim 8, wherein the program instructions further cause the processor to share the plurality of encrypted datasets with a federated cloud environment.