Anonymous encrypted data
First Claim
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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.
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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.
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Citations
9 Claims
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1. A system, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6)
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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:
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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. - View Dependent Claims (8, 9)
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Specification