Anonymous encrypted data

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.