What is Homomorphic Encryption? Data Security Explained

Traditional methods of cybersecurity involve decryption of the data prior to processing and analysis by applications. Nonetheless, even a few seconds of unencrypted sensitive files leave space for attacks by hackers or cloud providers who would be able to access the unencrypted information.

The homomorphic encryption technique assists in solving this issue by making calculations directly on ciphertexts and yielding a precise result at the same time without revealing the content.

What is Homomorphic Encryption? 

It is one of the most complex cryptographic techniques and enables analyzing encrypted data. Speaking about the name itself, it originates from the Greek language and actually means “same structure.” This is how the process takes place in a cloud analytics scenario:

• Scrambling of the Data: The company encrypts its sensitive financial figures using a private key and stores the data set in the cloud.
• Analysis of the Encrypted Text: The cloud executes complicated queries and algorithms on the scrambled text.
• Decryption of the Result: The cloud returns an encrypted result, and the organization decrypts it using its private key.

Three Main Homomorphic Schemes

Cryptography systems are classified according to the kinds and number of mathematical operations that can be performed using them:

• Partially Homomorphic Encryption (PHE)

  Allows only one type of mathematical operation to be conducted either indefinitely many times, which is either addition or multiplication. It suits simple calculations such as anonymous counting of votes in electronic voting systems.

• Somewhat Homomorphic Encryption (SHE)

  Allows both addition and multiplication mathematical operations but allows a limited number of consecutive operations due to encrypted noise.

• Fully Homomorphic Encryption (FHE)

  The ideal encryption method allowing any number of addition and multiplication operations.

Primary Applications in Vulnerable Industries

Encryption of ciphertexts safely creates great benefits for the private sector in terms of:

• Health Care and Medical Research: Artificial intelligence programs may be applied to patient files or genomes from various hospitals without infringing on stringent medical confidentiality laws.
• Anti-Fraud Analysis: Financial companies can compare transactions on other networks to detect fraud without disclosing any information about clients’ finances.
• Safe Cloud Computation: Companies are able to compute large amounts of data on public clouds without losing their sensitive information and client data.

Also Read: AI in Cybersecurity: How Machine Learning is Fighting Hackers

Conclusion

There are major weaknesses of traditional approaches to data security while data is being processed. Adopting homomorphic encryption is the solution to the problem that will allow the use of external cloud computing and artificial intelligence without risking sensitive data.