When communicating sensitive, confidential, or personal information over the Internet, encryption is a vital privacy tool. Even if hackers, cybercriminals, etc. are able to intercept it before it reaches its intended recipients, encryption scrambles plain text into a form of secret code that hackers or cybercriminals can’t decipher. When the communication reaches its intended recipients, they will have their own key to decode the data into plain, readable language. As a result, encryption can help protect the information you send, receive, and store on a device. Text messages on your smartphone, jogging records on your fitness watch, and financial information provided through your online account are all examples. This article will discuss two new emerging techniques of encryption which are homomorphic & polymorphic encryption. It will also discuss the uses of these techniques and how these differ from common techniques in terms of efficiency and safety.
Homomorphic Encryption and its uses
The difficulty with encrypting data is that you’ll have to decrypt it sooner or later. Furthermore, decrypting data exposes it to hackers. You can encrypt your cloud files with a secret key, but once you want to do something with them—anything from editing a word document to searching a database of financial data—you must unlock the data and expose it. Homomorphic encryption, a breakthrough in cryptography, has the potential to change that.
The goal of homomorphic encryption is to make it possible to compute on encrypted data. As a result, data can stay private while being analyzed, allowing beneficial tasks to be completed with data stored in untrustworthy contexts. This is a highly valuable capability in a world of distributed processing and heterogeneous networking. A homomorphic cryptosystem is similar to other types of public encryption in that it encrypts data with a public key and only allows the person with the matching private key to view the decrypted data. The fact that it uses an algebraic system to let you or others to do a range of computations (or operations) on the encrypted data sets it distinct from other types of encryption.
Types of Homomorphic Encryption
Homomorphic encryption is divided into three categories. The main distinction is between the types and frequency of mathematical operations that can be done on the ciphertext. The three types are as follows:
1. Partially Homomorphic Encryption – Only a few mathematical functions can be performed on encrypted values with partially homomorphic encryption (PHE). This indicates that the ciphertext can only perform one operation, either addition or multiplication, an endless number of times. PHE with multiplicative operations is the basis for RSA encryption, which is widely used in SSL/TLS connections.
2. Somewhat Homomorphic Encryption – A somewhat homomorphic encryption (SHE) system allows for select operations (either addition or multiplication) up to a particular level of complexity, but only a limited number of times.
3. Fully Homomorphic Encryption – Fully homomorphic encryption (FHE), while still in the development stage, has a lot of potential for making functionality consistent with privacy by helping to keep information secure and accessible at the same time. Developed from the SHE scheme, FHE is capable of using both addition and multiplication any number of times and makes secure multi-party computation more efficient. Unlike other forms of homomorphic encryption, it can handle arbitrary computations on your ciphertexts.
Uses of Homomorphic Encryption:
Several practical uses of FHE have already been identified by researchers, some of which are discussed here:
1. Keeping Data Safe in the Cloud – You can safeguard the data you store in the cloud while keeping the ability to calculate and search ciphered information that you can subsequently decode without jeopardizing the data’s integrity.
2. Using Data Analytics to Enable Regulated Industries – Homomorphic encryption protects the privacy of users and patients by encrypting data and sending it to commercial cloud environments for research and data sharing. It can be used by businesses and organizations in a range of industries, including financial services, retail, information technology, and healthcare, to let users access data without viewing the unencrypted values. Predictive analysis of medical data without jeopardizing data privacy, consumer privacy in personalized advertising, financial privacy for stock price prediction algorithms, and forensic image recognition are all examples.
3. Increasing the security and transparency of elections – Researchers are investigating how to employ homomorphic encryption to improve the security and transparency of democratic elections. The Paillier encryption technique, for example, which uses addition operations, is ideally suited for voting-related applications because it allows users to add up diverse values in an unbiased manner while keeping their values private. This technique could not only protect data against alteration but also allow authorized third parties to independently verify it.
Polymorphic Encryption and its uses
The traditional method of protecting sensitive data has been to de-identify the data using a variety of encryption and tokenization solutions, guaranteeing that it cannot be abused if it is exposed. The problem with this technique is that once data is encrypted, it is useless to the apps and tools that rely on it to run the company. Because typical encryption systems do not distinguish between the types of data they encrypt, this problem arises. An encrypted phone number is the same as an encrypted email address or social security number. Most encryption solutions have taken this technique for decades.
Each form of sensitive data is a distinct data structure made up of various components with various applications. If we can identify those use-cases ahead of time, we can conduct processes on fully encrypted data using various encryption, tokenization, and redaction techniques. This is known as Polymorphic Encryption.
For example, if we look closely at a phone number, we notice that it’s actually a complex data type made up of three distinct structures. A country code, an area code, and a local code are all present. Each of these components serves a distinct purpose. We can use the country code to figure out how many foreign users it has or where an incoming call should be routed. The area code can be used to segment other attributes such as revenue by area code or to understand client dispersion by area code. Finally, a masked version of the local code may be displayed to validate the correct phone number for multi-factor authentication.
We can break the phone number into its components and encrypt each one separately using polymorphic encryption, allowing us to perform operations on the data without having to decode it. For example, we could encrypt the area code with a method that allows us to discover records in our database with the same area code using precise matches on fully encrypted data. The income data can then be encrypted with another method that allows us to average the earnings in that area code. Without ever decrypting the data, you may now get an estimate of the typical salary by area code.
Therefore, in an era where privacy is becoming more important, thanks to regulations like GDPR, the concept of homomorphic and polymorphic encryption holds a lot of potential for real-world applications in a range of industries. One of the most intriguing elements is how it balances the requirement for privacy protection with the need for more extensive research.