Cyber Security

Encryption has been a superior method of data protection for decades. It involves encoding information so that only authorized parties can access it. However, traditional encryption has limitations in its process. This is why various types of encryption, including Homomorphic Encryption, have been developed.

Imagine placing your data in a locked box. Even though the box is closed, you can still perform calculations on the data inside without unlocking it. This is the basic principle behind homomorphic encryption.

If you’re still curious about it and want to explore more as you plan to adapt it to your security system, keep following this article!

What is Homomorphic Encryption?

Homomorphic encryption (HE) is a type of encryption that allows authorized users to perform mathematical operations on data without the need to decrypt it first.

Decrypting private data is usually necessary to access or manipulate it. However, this process can expose the data to potential security risks, such as unauthorized access or data breaches. This encryption method helps minimize the opportunity for third parties to access sensitive information during computations, enhancing the security and privacy of the data.

Types of Homomorphic Encryption

The homomorphic encryption scheme has revolutionized data security by allowing computations to be performed on encrypted data without the need for decryption. However, there are different types of homomorphic encryption that offer varying levels of functionality and security. These include:

Somewhat Homomorphic Encryption (SHE)

Somewhat homomorphic encryption is suitable for simple calculations and basic statistics. It lets you add and multiply encrypted data simultaneously but has limits. You can only perform a certain number of operations, and the computation may worsen as more operations are carried out.

Partially Homomorphic Encryption (PHE)

Partially Homomorphic Encryption is the simplest type of this encryption method. It can perform additions or multiplications on the encrypted data, but not simultaneously. This type of encryption is often used in scenarios where only one kind of operation is needed, such as in some basic mathematical computations or protocols.

Fully Homomorphic Encryption (FHE)

Fully homomorphic encryption (FHE) is the most advanced form of homomorphic encryption, enabling authorized users to perform unlimited computations on plaintext data. However, FHE is currently costly, making it impractical for many applications.

Homomorphic Encryption Use Cases

A homomorphic encryption system has several advantages that make it used in many cases. Here are some of the examples:

Secure Cloud Computation

Cloud computing is becoming increasingly popular for storing data. However, traditional encryption methods used in cloud-based computation often require decrypting the data before performing any operations, which can compromise security. By using homomorphic encryption, computations can be performed directly on encrypted data, enhancing the cloud network security. 

Secure Voting

There’s a type of homomorphic encryption called Paillier encryption that could be used in voting systems. With this encryption, voters could maintain their privacy regarding whom they are voting for. The encryption would enable votes to be added up without anyone seeing individual votes. Third parties could also verify that the total number of votes is correct without knowing who voted for whom.

Supply Chain Security

Businesses often rely on contractors, vendors, or third parties to operate smoothly. While this is necessary, it’s crucial to maintain trust and be cautious when sharing sensitive information or granting access to critical systems. These external parties are outsiders, so we must handle their involvement carefully. Implementing homomorphic encryption can help mitigate these risks by using encrypted data within a company’s backend systems.

Secure Artificial Intelligence/Machine Learning

AI/ML has been widely adopted, but it relies on accessing and learning from data. This can be problematic if the data cannot be accessed easily. This is where homomorphic encryption can help. This approach ensures data privacy and encourages individuals to share their data with AI projects, which is crucial for the effectiveness of these algorithms. One example is the use of AI/ML in medical research, where patients’ data is essential for developing treatments and improving outcomes, yet it must be kept private.

Regulatory Compliance

In business, innovation is crucial, and one way to find inspiration is by gathering data from customers or visitors. However, data privacy regulations like the General Data Protection Regulation (GDPR) can sometimes restrict this practice. Homomorphic encryption provides a solution by enabling businesses to offer more online services to people while still complying with regulations and ensuring the security of their data.

Benefits of Homomorphic Encryption

We’ve already discussed the general information about this encryption type. Now, let’s explore its benefits and how it can help organizations safeguard their data.

Collaborate confidently on hybrid cloud

Homomorphic encryption provides a solution for hybrid cloud security, allowing you to encrypt data in public and private clouds as well as third-party environments. With this approach, you can securely encrypt your data across various platforms and environments without the need to worry about maintaining confidentiality controls.

Enable AI, analytics, and machine learning (ML)

AI/ML works by analyzing the data provided to it. Once it understands the patterns and trends in the data, it can make predictions and decisions based on that analysis. However, some data can be sensitive and should not be accessed by anyone. This is where homomorphic encryption can help, as it allows computation to be performed on the data without decrypting it.

Gain valuable insights

Homomorphic encryption offers an advantage by allowing businesses to gain measurable economic benefits. It enables different entities, such as business lines and third parties, to perform big data analytics on encrypted data. This ensures that confidential information remains private and secure while permitting valuable data analysis for decision-making purposes.

Frequently Asked Questions

Conclusion

Managing sensitive data is like walking a tightrope. Just one misstep can lead to serious consequences, including data breaches, privacy violations, and legal issues. Careful planning is required to ensure that sensitive information remains protected at all times, and homomorphic encryption is one way to do this.

Now that you’ve understood this encryption method better, are you interested in strengthening your cyber security? Contact Fluxgate today to discuss how we can help secure your sensitive data and protect your business from cyber threats.