Anonymous encrypted data
First Claim
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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.
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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
8 Claims
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1. A computer-implemented method, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification