In this article, we will discuss what homomorphic encryption is and the key advantages of homomorphic encryption.
What is Homomorphic Encryption?
A cutting-edge technique called fully homomorphic encryption (FHE) can assist you in achieving zero trust by revealing the value of data on untrusted domains without requiring its decryption.
The hybrid multicloud environments in which today’s company data is housed expose it to a number of security and privacy threats. Even while encryption offers security, sensitive information usually needs to be decrypted before it can be accessed for computing and other vital business functions.
This creates the possibility of confidentiality and privacy controls being compromised. Up until recently, dealing with third parties and the cloud has come at the expense of those vulnerabilities.
Because the data is constantly encrypted and may be shared, even on cloud domains that aren’t trusted, completely homomorphic encryption makes it easier to enforce zero trust because the people doing the computation can’t read it.
In summary, high-value analytics and data processing can now be carried out by internal or external parties without necessitating the disclosure of the data.
Advantages of Homomorphic encryption
Get insightful knowledge
Allow business divisions and outside parties to do big data analytics on encrypted data while upholding privacy and compliance regulations to produce quantifiable financial gains.
Work together with assurance on a hybrid cloud
Process encrypted data while upholding confidentiality constraints in third-party contexts and public and private clouds.
Make machine learning (ML), analytics, and AI possible
To calculate on encrypted data without disclosing private information, use AI and ML.
Use cases for homomorphic encryption
Predictive analysis using encryption in financial services
Regulations and policies frequently prohibit organizations from sharing and mining sensitive data, even though machine learning (ML) aids in the creation of predictive models for conditions ranging from financial transaction fraud to investment results. FHE makes it possible to use ML models to compute encrypted data without disclosing the data.
Privacy in the life sciences and healthcare
Even though the cloud is effective at handling workloads for large clinical trials, hospitals are frequently unable to make the switch due to privacy concerns and healthcare restrictions. FHE can speed up learning from real-world data, expand sample sizes in clinical research, and enhance acceptability of data-sharing methods.
Consumer services and retail encrypted search
Large-scale monitoring of customer search and information access is made possible by technology, yet businesses find it challenging to profit from this data due to privacy rights. While safeguarding the right to privacy and hiding user inquiries, FHE enables the acquisition of insights into consumer behavior.
Intel Homomorphic Encryption Toolkit
The way multiple parties interact with and share datasets for analysis is completely transformed by homomorphic encryption (HE).
This makes it possible to obtain insightful information with a lower chance of disclosing private information or jeopardising confidentiality and trade secrets.
Summary
A well-tuned hardware and software solution that improves the performance of HE-based cloud solutions running on the newest Intel platforms is what the Intel Homomorphic Encryption Toolkit (Intel HE Toolkit) is intended to offer. By offering cutting-edge HE technology on Intel architecture, the goal is to spearhead the homomorphic encryption revolution and enable clients to obtain insightful knowledge while safeguarding extremely sensitive and private information.
By speeding HE to satisfy commercial performance requirements for real-world future use cases, Intel is facilitating the emergence of the HE ecosystem. In order to utilise the most recent Intel Advanced Vector Extensions 512 (Intel AVX-512) acceleration instructions, the toolkit was created as an extendable solution. Future specially designed accelerator technologies can also be integrated with the toolkit.
What the Toolkit Contains
The Intel HE Acceleration Library, also known as the Intel Homomorphic Encryption Acceleration Library, uses optimised Intel AVX-512 implementations of the lattice cryptography kernels used in HE. Performance on the newest Intel Xeon Scalable processors has been optimised for the functions.
- The acceleration library was incorporated with a version of Microsoft SEAL.
- The acceleration library is integrated with a version of the PALISADE Homomorphic Encryption Software Library.
- HE implementation examples and references:
- Micro benchmarks for fundamental operations in homomorphic encryption
- Examples of kernels that demonstrate the homomorphic implementation of higher-level operations like matrix multiplication
- Examples of applications that demonstrate various use cases and implementations
- User manuals and technical documents
System prerequisites
C++ is the language.
System prerequisites:
- Utilises Intel hardware platforms
- Suggested for optimal performance: Scalable processors from Intel Xeon
System software: Linux, Ubuntu
