The increasing relevance of Machine Learning raises concerns about how the cloud provider will handle private and secret data, although homomorphic encryption has made it more secure for businesses to use the cloud providers’ services.
Today, protecting sensitive data from vulnerabilities is quite a big challenge, and even after being cautious and well-prepared to keep data safe specifically in online accounts, the threat of information leaks or data theft is persistent.
However, the new privacy-protecting revolution has been introduced by the research that puts the strengths of Homomorphic Encryption (HE) and Machine Learning together, to create a better defense.
The prevalent issue that AI products and services face, is that it relies on Machine Learning systems that collect sensitive and personal data. Maintaining privacy becomes a major concern. Although these data are the new source of revenue generation for most companies by using data at their disposal meanwhile protecting their privacy with the homomorphic encryption solution.
The homomorphic encryption ability to let computations access encrypted data directly has made it possible to run machine learning on anonymized datasets without losing context.
As public-key encryption requires decrypted data before any manipulation but due to current encryption algorithms, the data processing without first decrypting is not possible, and following data privacy rules are not compliant.
But data is vulnerable to unwanted access if it has been encrypted using any other method since it has to be decrypted before processing. But with the help of HE, data integrity and privacy are pre-protected before data processing, as well as homomorphic encryption, which lets encrypted data processing even in an encrypted state.
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Business-wise, it has been feasible to assign a computer service to carry out a machine learning algorithm while maintaining the privacy of the training and test sets of data. This machine learning execution has been a major game-changer for the cloud industry.
The involvement of homomorphic encryption has made it possible that encrypted data sent by a user to the cloud using API and to receive encrypted results from the machine learning models. During this whole process, no data gets decrypted or stored in the cloud. Hence, the user data is saved from the cloud provider.
The significant point here is that machine learning adoption brings a concern about private and confidential data being handled by the cloud provider but leveraging homomorphic encryption has made it reliable for the companies to opt for the cloud companies’ services.
The deployment of homomorphic encryption in machine learning has created a whole new data industry, bringing enterprises altogether that hold data with companies or individuals who need data. This has allowed existing sensitive or regulated data assets to be used in ways that were not thought possible earlier. Leveraging homomorphic encryption helps enterprises to securely monetize data, while preserving the privacy of both customers and data.
The industry can expect more specialized startups to create data marketplaces that enable enterprises to use homomorphic encryption for data selling. The familiar problem is that AI vendor always deals with issues of data availability.
In numerous cases, AI vendors need more data to make proof of concept successful but this is not always possible due to legal reasons by utilizing homomorphic encryption, this problem has been resolved as it gives access to the additional data demonstrating AI vendors’ algorithms on real data.
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Machine learning with Homomorphic Encryption has created a new business revolution that has enabled organizations to collaborate securely across enterprises or jurisdictional boundaries without any new sensitive variables in the organization’s data holdings. This exposure is crucial for triggering additional reporting requirements or exposing competitive advantage.
While organization follows this data protection process, HE deployments let enterprises securely utilize the external data assets in a decentralized manner without being exposed to sensitive indicators. This has help technology to be configured, allowing them to continue the access and verification controls that are established by the data owners.
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