System and method to dynamically encrypt data
An apparatus comprises a memory communicatively coupled to a processor. The memory is configured to store one or more machine learning algorithms associated with encrypting data in accordance with one or more machine learning models. The processor is configured to generate a first hash of shareable data and encrypt the shareable data based at least in part upon one or more keys upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models. Further, the processor is configured to generate a second hash of an encrypted shareable data, combine the first hash and the second hash into a combined sender hash, encrypt the combined sender hash, and transmit an encrypted combined sender hash and the encrypted shareable data to a receiver.
1 . An apparatus, comprising:
a memory configured to store:
one or more machine learning algorithms associated with encrypting data in accordance with one or more machine learning models; and
a processor communicatively coupled to the memory and configured to:
receive one or more keys;
receive a first request for first shareable data;
retrieve the first shareable data requested;
generate a first hash of the first shareable data;
upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models, encrypt the first shareable data based at least in part upon the one or more keys;
generate a second hash of a first encrypted shareable data;
combine the first hash and the second hash into a first combined sender hash;
encrypt the first combined sender hash; and
transmit a first encrypted combined sender hash and the first encrypted shareable data to a first receiver, wherein:
the first encrypted combined sender hash is an encrypted version of the first combined sender hash; and
the first encrypted shareable data is an encrypted version of the first shareable data.
2 . The apparatus of claim 1 , wherein the one or more keys comprise a public key associated with the first receiver.
3 . The apparatus of claim 2 , wherein the one or more machine learning algorithms are executed by the processor in accordance with a machine learning model that is trained based at least in part upon the public key associated with the first receiver.
4 . The apparatus of claim 1 , wherein the processor is further configured to:
in conjunction with encrypting the first shareable data, determine a number of data bits in the first shareable data; and
upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models, calculate a plurality of coordinates configured to change one or more data bits in the first shareable data.
5 . The apparatus of claim 4 , wherein the processor is further configured to:
in conjunction with calculating the plurality of coordinates configured to change the one or more data bits in the first shareable data, add a plurality of data bits inside the first shareable data; and
in response to adding the plurality of data bits inside the first shareable data, manipulate a total number of data bits in the first shareable data.
6 . The apparatus of claim 1 , wherein the one or more keys are received from user device communicatively coupled to the first receiver.
7 . The apparatus of claim 1 , wherein the one or more keys are received from the first receiver.
8 . The apparatus of claim 1 , wherein the processor is further configured to:
receive a second request for second shareable data;
retrieve the second shareable data requested;
generate a third hash of the second shareable data;
upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models, encrypt the second shareable data based at least in part upon the one or more keys;
generate a fourth hash of a second encrypted shareable data;
combine the third hash and the fourth hash into a second combined sender hash;
encrypt the second combined sender hash; and
transmit a second encrypted combined sender hash and the second encrypted shareable data to a second receiver, wherein:
the second encrypted combined sender hash is an encrypted version of the second combined sender hash; and
the second encrypted shareable data is an encrypted version of the second shareable data.
9 . The apparatus of claim 8 , wherein the first receiver and the second receiver are a same receiver.
10 . The apparatus of claim 8 , wherein the second combined sender hash is encrypted in conjunction with the second shareable data.
11 . A method, comprising:
receiving one or more keys;
receiving a first request for first shareable data;
retrieving the first shareable data requested;
generating a first hash of the first shareable data;
upon executing one or more machine learning algorithms in accordance with one or more machine learning models, encrypting the first shareable data based at least in part upon the one or more keys;
generating a second hash of a first encrypted shareable data;
combining the first hash and the second hash into a first combined sender hash;
encrypting the first combined sender hash; and
transmitting a first encrypted combined sender hash and the first encrypted shareable data to a first receiver, wherein:
the first encrypted combined sender hash is an encrypted version of the first combined sender hash; and
the first encrypted shareable data is an encrypted version of the first shareable data.
12 . The method of claim 11 , wherein the one or more keys comprise a public key associated with the first receiver.
13 . The method of claim 12 , wherein the one or more machine learning algorithms are executed in accordance with a machine learning model that is trained based at least in part upon the public key associated with the first receiver.
14 . The method of claim 11 , further comprising:
in conjunction with encrypting the first shareable data, determining a number of data bits in the first shareable data; and
upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models, calculating a plurality of coordinates configured to change one or more data bits in the first shareable data.
15 . The method of claim 14 , further comprising:
in conjunction with calculating the plurality of coordinates configured to change the one or more data bits in the first shareable data, adding a plurality of data bits inside the first shareable data; and
in response to adding the plurality of data bits inside the first shareable data, manipulating a total number of data bits in the first shareable data.
16 . A non-transitory computer readable medium storing instructions that when executed by a processor cause the processor to:
receive one or more keys;
receive a first request for first shareable data;
retrieve the first shareable data requested;
generate a first hash of the first shareable data;
upon executing one or more machine learning algorithms in accordance with one or more machine learning models, encrypt the first shareable data based at least in part upon the one or more keys;
generate a second hash of a first encrypted shareable data;
combine the first hash and the second hash into a first combined sender hash;
encrypt the first combined sender hash; and
transmit a first encrypted combined sender hash and the first encrypted shareable data to a first receiver, wherein:
the first encrypted combined sender hash is an encrypted version of the first combined sender hash; and
the first encrypted shareable data is an encrypted version of the first shareable data.
17 . The non-transitory computer readable medium of claim 16 , wherein the one or more keys comprise a public key associated with the first receiver.
18 . The non-transitory computer readable medium of claim 17 , wherein the one or more machine learning algorithms are executed in accordance with a machine learning model that is trained based at least in part upon the public key associated with the first receiver.
19 . The non-transitory computer readable medium of claim 16 , wherein the instructions further cause the processor to:
in conjunction with encrypting the first shareable data, determine a number of data bits in the first shareable data; and
upon executing the one or more machine learning algorithms in accordance with the one or more machine learning models, calculate a plurality of coordinates configured to change one or more data bits in the first shareable data.
20 . The non-transitory computer readable medium of claim 19 , wherein the instructions further cause the processor to:
in conjunction with calculating the plurality of coordinates configured to change the one or more data bits in the first shareable data, add a plurality of data bits inside the first shareable data; and
in response to adding the plurality of data bits inside the first shareable data, manipulate a total number of data bits in the first shareable data.