Anonymous encrypted data

Patent metadata

Field	Value
Publication number	US-10700866-B2
Application number	US-201715842475-A
Country	US
Kind code	B2
Filing date	Dec 14, 2017
Priority date	Jul 12, 2017
Publication date	Jun 30, 2020
Grant date	Jun 30, 2020

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.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
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, wherein the machine learning algorithm is a distance based clustering algorithm based on a location identifier of geographical coordinates;
modifying, by the system, the plurality of clusters based on a defined security requirements that facilitates anonymity of the encrypted data, wherein the modifying 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 computer-implemented method of claim 1 , wherein the defined security requirements set a minimum number of members per cluster from the plurality of clusters.

3. The computer-implemented method of claim 1 , wherein the modifying further comprising suppressing a cluster from the plurality of clusters based on a suppression threshold that designates an amount of encrypted data from the encrypted dataset to be removed.

4. The computer-implemented method of claim 3 , wherein the suppressing comprising:
identifying, by the system, encrypted data within the cluster to be removed based on a location indicator associated with the encrypted data;
removing, by the system, the identified encrypted data from the encrypted dataset to generate a second encrypted dataset; and
generating, by the system, a second plurality of clusters of encrypted data from the second encrypted dataset using the machine learning algorithm.

5. The computer-implemented method of claim 4 , wherein the modifying further comprises re-assigning the encrypted data of the second encrypted dataset from a first cluster from the plurality of clusters to a second cluster of the plurality of clusters based on a parameter.

6. The computer-implemented method of claim 3 , wherein the suppressing comprising removing the cluster from the plurality of clusters.

7. The computer-implemented method of claim 1 , wherein the modifying further comprising re-assigning the encrypted data from a first cluster from the plurality of clusters to a second cluster of the plurality of clusters based on a parameter.

8. The computer-implemented method of claim 7 , wherein the parameter is a location indicator associated with encrypted data being re-assigned.

Assignees

• IBM

Inventors

• Antonatos Spyridon
• Braghin Stefano
• Kaul Akshar
• Kesarwani Manish
• Mehta Sameep

Classifications

• H04L63/0421

  Anonymous communication, i.e. the party's identifiers are hidden from the other party or parties, e.g. using an anonymizer · CPC title

• H04L63/0428

  wherein the data content is protected, e.g. by encrypting or encapsulating the payload · CPC title

• H04L9/0894

  Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage · CPC title

• G06F21/6245 Primary

  Protecting personal data, e.g. for financial or medical purposes · CPC title

• G06F12/1408

  by using cryptography (for digital transmission H04L9/00) · CPC title