Current encryption technologies play a crucial role in today's digital world. They support the secure functioning of the Internet, form the foundation of Web3, and enable individuals to safeguard their personal information. Yet, traditional encryption methods have a significant drawback—data typically must be decrypted before it can be analyzed or processed. Unfortunately, decrypting sensitive data to perform computations exposes it to third parties, defeating the purpose of encryption.
Homomorphic encryption addresses this problem by allowing computations to occur on encrypted data itself. This means cloud providers and web-based services can process your data without ever having direct access to the underlying sensitive information. For organizations and individuals concerned about privacy, homomorphic encryption provides enhanced functionality while maintaining robust data security.
What Is Homomorphic Encryption?
Outsource the computation of a function f(x) on data x to a server, without revealing the data to the server.
Imagine being able to securely analyze sensitive data—such as your medical history, financial records, or personal details—without ever showing it directly to third parties.
Homomorphic encryption is an innovative cryptographic method that allows computations to be performed on encrypted data, eliminating the need to decrypt it first. This means your data stays protected and encrypted, even as external services analyze it.
Here's why that's exciting: Suppose you want insights from your genetic data. Currently, using genetic testing services typically involves sharing highly sensitive information. With homomorphic encryption, these services could analyze your encrypted DNA without ever directly accessing your genetic sequences—providing valuable insights while protecting your privacy.
Though first proposed in 1978 by Rivest, Adleman, and Dertouzos, homomorphic encryption became practical only in 2009, thanks to computer scientist Craig Gentry.
Gentry describes the idea vividly, comparing homomorphic encryption to having a "sealed glove box":
“Imagine you have a sealed glove box containing sensitive materials. Anyone can slide their hands into the gloves attached to the outside, manipulate and rearrange the objects inside the box—but they can never remove or directly access what’s inside. When the work is complete, only the owner with the key can unlock the box and retrieve the processed information.”
Homomorphic Encryption Use Cases
Computing AI algorithms on encrypted data is possible thanks to homomorphic encryption.
Diagram By Chainlink
Secure Artificial Intelligence/Machine Learning
Artificial Intelligence thrives on vast amounts of data. But often, the most valuable datasets—such as medical records or financial data—remain inaccessible due to privacy concerns. Homomorphic encryption solves this by enabling AI models to learn from encrypted data, never needing access to the raw information.
Example:
Imagine an AI startup wants to develop a powerful predictive model that can accurately forecast financial risks, fraud, and creditworthiness based on spending patterns and bank transaction histories. Naturally, banks have strict privacy regulations and cannot simply share sensitive customer transactions and financial details directly with an external AI company.
Traditionally, such collaboration would be impossible without exposing private data—something banks and customers alike would never accept. But with homomorphic encryption, banks can securely share encrypted financial transaction data with the AI startup. The AI company can then run its predictive algorithms on these encrypted records without ever actually seeing individual transaction details.
2. Secure Cloud Computing
Traditionally, cloud providers must have direct access to your data to perform computations, creating significant privacy risks. Homomorphic encryption changes the game entirely, allowing cloud providers to process encrypted data without ever decrypting it.
3. Regulatory Compliance
Businesses worldwide face increasing regulatory requirements like GDPR, emphasizing stringent privacy standards. Homomorphic encryption helps companies comply with these regulations while still offering innovative digital services.
4. Secure and Transparent Voting Systems
Homomorphic encryption provides an ideal solution for confidential, transparent, and verifiable voting systems. Votes remain encrypted and anonymous, while still being accurately checked and auditable by external entities.
5. Enhancing Supply Chain Security**
Supply chains involve sharing sensitive data across multiple partners, including vendors, logistics providers, and contractors. This shared data creates vulnerabilities and potential entry points for cyberattacks. Homomorphic encryption can eliminate these vulnerabilities by encrypting
sensitive information end-to-end.
Conclusion
Homomorphic encryption isn’t just a technical curiosity—it's a fundamental shift in how we balance the power of data analytics with the necessity of data privacy.
As more organizations and individuals prioritize privacy, homomorphic encryption offers an exciting path forward: harnessing the full value of data without compromising confidentiality. This isn't merely encryption as usual; it's the future of secure collaboration and trust.
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