Systems and methods for homomorphic encryption-based data caching
The present disclosure details systems and methods for homomorphic encryption-based data caching. This technology detects transaction patterns via a monitoring module that liaises with both a payment system and an entity system. Prior to its processing, transaction data is encrypted with a homomorphic encryption module. A dynamic interface, aligned with a local point-of-sale (POS) cache, prioritizes data based on current transaction metrics. If a recipient server is busy, the transaction data is rerouted, stored in the POS's local cache, and queued. The transaction's success is gauged against a predefined cut-off time. Unsuccessful transactions within this window are slated for batch processing, yet resource processing proceeds while awaiting this batch processing.
1 . A system for homomorphic encryption-based data caching, the system comprising:
a processing device;
a non-transitory storage device containing instructions when executed by the processing device, causes the processing device to perform the steps of:
monitoring transaction patterns in communication with both a payment system and an entity system;
encrypting transaction data via a homomorphic encryption module for a transaction prior to processing the transaction data;
generating a dynamic cache optimization interface with a local point-of-sale (POS) cache, wherein the dynamic cache optimization interface determines data availability based on real-time transaction loads and patterns;
detecting a recipient server as occupied, and deferring the transaction data to a payment routing mechanism, storing the transaction data in the local POS cache, and placing the transaction in a queue;
evaluating, based on a predefined cut-off time, if the transaction is successful;
if the transaction is determined to be unsuccessful within the predefined cut-off time, placing the transaction in a batch processing queue; and
processing resources according to the transaction while it resides in the batch processing queue.
2 . The system of claim 1 , wherein the homomorphic encryption module encrypts the transaction data via a lattice-based encryption scheme such that additive and multiplicative computations on the transaction data can be conducted while the transaction data remains encrypted.
3 . The system of claim 1 , wherein the dynamic cache optimization interface maintains a local cache specifically for the real-time transaction loads and patterns.
4 . The system of claim 1 , wherein the local POS cache is dynamically adjusted based on both a current transactional volume and a predictive analysis.
5 . The system of claim 1 , wherein a long short-term memory (LTSM) neural net continually refines a prediction of server latency or transaction delay of the entity system based on a blend of historical data and real-time data.
6 . The system of claim 1 , wherein upon detecting the recipient server as busy, the system further analyzes a cause for the recipient server being busy and employs an adaptive retry mechanism that adjusts a time interval between subsequent transaction attempts.
7 . The system of claim 1 , wherein the payment routing mechanism employs a multi-path routing algorithm, selecting optimal routes for transaction data based on network congestion, server availability, and transaction priority levels.
8 . A computer program product for homomorphic encryption-based data caching, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:
monitor transaction patterns in communication with both a payment system and an entity system;
encrypt transaction data via a homomorphic encryption module for a transaction prior to processing the transaction data;
generate a dynamic cache optimization interface with a local point-of-sale (POS) cache, wherein the dynamic cache optimization interface determines data availability based on real-time transaction loads and patterns;
detect a recipient server as occupied, and deferring the transaction data to a payment routing mechanism, storing the transaction data in the local POS cache, and placing the transaction in a queue;
evaluate, based on a predefined cut-off time, if the transaction is successful;
if the transaction is determined to be unsuccessful within the predefined cut-off time, place the transaction in a batch processing queue; and
process resources according to the transaction while it resides in the batch processing queue.
9 . The computer program product of claim 8 , wherein the homomorphic encryption module encrypts the transaction data via a lattice-based encryption scheme such that additive and multiplicative computations on the transaction data can be conducted while the transaction data remains encrypted.
10 . The computer program product of claim 8 , wherein the dynamic cache optimization interface maintains a local cache specifically for the real-time transaction loads and patterns.
11 . The computer program product of claim 8 , wherein the local POS cache is dynamically adjusted based on both a current transactional volume and a predictive analysis.
12 . The computer program product of claim 8 , wherein a long short-term memory (LTSM) neural net continually refines a prediction of server latency or transaction delay of the entity system based on a blend of historical data and real-time data.
13 . The computer program product of claim 8 , further comprising, upon detecting the recipient server as busy, analyzing a cause for the recipient server being busy and employing an adaptive retry mechanism that adjusts a time interval between subsequent transaction attempts.
14 . The computer program product of claim 8 , wherein the payment routing mechanism employs a multi-path routing algorithm, selecting optimal routes for transaction data based on network congestion, server availability, and transaction priority levels.
15 . A method for homomorphic encryption-based data caching, the method comprising:
monitoring transaction patterns in communication with both a payment system and an entity system;
encrypting transaction data via a homomorphic encryption module for a transaction prior to processing the transaction data;
generating a dynamic cache optimization interface with a local point-of-sale (POS) cache, wherein the dynamic cache optimization interface determines data availability based on real-time transaction loads and patterns;
detecting a recipient server as occupied, and deferring the transaction data to a payment routing mechanism, storing the transaction data in the local POS cache, and placing the transaction in a queue;
evaluating, based on a predefined cut-off time, if the transaction is successful;
if the transaction is determined to be unsuccessful within the predefined cut-off time, placing the transaction in a batch processing queue; and
processing resources according to the transaction while it resides in the batch processing queue.
16 . The method of claim 15 , wherein the homomorphic encryption module encrypts the transaction data via a lattice-based encryption scheme such that additive and multiplicative computations on the transaction data can be conducted while the transaction data remains encrypted.
17 . The method of claim 15 , wherein the local POS cache is dynamically adjusted based on both a current transactional volume and a predictive analysis.
18 . The method of claim 15 , wherein a long short-term memory (LTSM) neural net continually refines a prediction of server latency or transaction delay of the entity system based on a blend of historical data and real-time data.
19 . The method of claim 15 , further comprising, upon detecting the recipient server as busy, analyzing a cause for the recipient server being busy and employing an adaptive retry mechanism that adjusts a time interval between subsequent transaction attempts.
20 . The method of claim 15 , wherein the payment routing mechanism employs a multi-path routing algorithm, selecting optimal routes for transaction data based on network congestion, server availability, and transaction priority levels.