Innovative Machine Learning Applications for Cryptography: Encryption Techniques in Machine Learning - A Concise Overview

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One of the key frontiers in modern data security is the merging of encryption and machine learning (Bandaru et al., 2020). Technology, healthcare, and finance are just a few of the industries that have transformed due to machine learning's ability to extract insights from large datasets (Visalakshi et al., 2021). But this data analysis capacity also brings up security and privacy issues (Anees et al., 2022). An essential security measure is encryption, which transforms private data into unreadable code that cannot be accessed by outside parties (Nitaj & Rachidi, 2023). Encryption is used by sectors including healthcare and finance to safeguard financial information and medical records against online attacks (Murthy et al., 2019). Encryption in healthcare protects patient privacy and complies with legal requirements like HIPAA (Ding et al., 2021). It also makes it easier to share data securely for research reasons. In the financial industry, encryption protects customer data and transactional data, increasing trust in digital systems (Visalakshi, 2022). Moreover, encryption promotes trust in digital interactions by protecting trade secrets and intellectual property owned by corporations (Visalakshi et al., 2013). Studies have concentrated on fusing machine learning and encryption methods to protect data privacy and facilitate perceptive analysis (Liu et al., 2022). Novel approaches like zero-knowledge proofs and secure enclave technologies are being explored by encryption technology advancements to tackle new security concerns (Badr, 2022).