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How does Homomorphic Encryption help Secure Data Processing?

In simple terms, Homomorphic encryption is a technique to pass information or messages securely in encrypted forms until it reaches the receiver. Homomorphic encryption works on encrypted data.

Homomorphic encryption operates the same as other encryption algorithms that use public key cryptography. It utilizes a public key to encrypt the data and only allows access to the corresponding private key to decrypt the data or information.


In simple mathematics, homomorphic refers to the conversion of data from one format to another while maintaining the integrity of data on both sides.

Most homomorphic encryption algorithms perform best in real-world applications when data is expressed as integers and operations such as addition and multiplication are used. This permits encrypted data to be analyzed and altered as if it were plaintext, without the need to decrypt it.

The encrypted data can be computed and processed to provide an encrypted answer, but only the private key holder can decrypt the ciphertext and comprehend its meaning. 

Why is homomorphic encryption used in cryptography?

Organizations may use traditional encryption techniques for the security of the data when stored in the cloud. But to check if the data is really secured and encrypted cloud providers like Amazon, Azure, etc use different encryption techniques to store different data.


Homomorphic encryption allows organizations to perform mathematical operations on encrypted data without disclosing it. With homomorphic encryption, the cloud service provider can only access encrypted data and execute computations without having to decrypt it.

The result of computation when decrypted is the same as what was there before encryption in plaintext. So privacy and security of data is maintained over the cloud servers.

Types of Homomorphic Encryption

Homomorphic encryption falls into three categories namely partially, somewhat, and fully encryption on the basis of computation speed.

Partially Homomorphic: Partially homomorphic encryption methods allow only one operation that can be performed on the ciphertext. This technique allows only addition or multiplication operations in order to establish a secure connection to security protocols.

For example, if encrypted values E(p) and E(q) are given then it can be computed as E(p+q) without decryption and vice versa for subtraction. In the case of multiplication if given encrypted values are E(x) and E(y), the computation will be E(x*y).

Fully homomorphic encryption:  Fully homomorphic encryption allows both the operation that can be performed on the ciphertext. It can allow both addition and multiplication operations on the data in order to encrypt the information. It also allows boolean operation for data encryption.


Examples include logic gate boolean operation Y = A.B so first of all multiplication is performed between operators and then the result is reversed in the case of not gate.

The goal of fully homomorphic encryption is to utilize the encrypted data to perform valuable operations without an encryption key, so techniques are usually used to improve cloud security. Fully homomorphic encryption allows functionality like retrieving the data and searching the data and users can modify the data without interaction with cloud providers.

Somewhat Homomorphic Encryption: Somewhat Homomorphic Encryption grants only a limited variety of operations on encrypted data. It includes paillier cryptography, which helps in addition to homomorphism, and Elgamal crypto which can do a multiplicative homomorphism.

Application of Fully Homomorphic Encryption

Cloud data storage: Using homomorphic encryption data stored in the cloud has security levels added to it.  In addition to the security, searching can be done here and later decryption steps are performed.

Data analytics: Homomorphic encryption permits statistics to be encrypted and outsourced to business cloud environments for studies and statistics-sharing functions at the same time as a defensive consumer or affected person statistics privateness.

It may be used for organizations and corporations throughout quite a few industries such as monetary services, retail, statistics technology, and healthcare to permit human beings to apply statistics without seeing its unencrypted values.

Examples encompass predictive evaluation of clinical statistics without setting statistics privateness at risk, retaining client privateness in personalized advertising, monetary privateness for capabilities like inventory rate prediction algorithms, and forensic photograph recognition.

Security and transparency in voting: People have started working towards improvement and security in the area of voting.  For example, the paillier encryption method allows users to add values in an unbiased way while keeping it private. So protecting the data against manipulation and it can be verified by anyone.

Health care: Under the standard called HIPAA, which is a health insurance portability and accountability act, the data of medical patients can be protected by implementing homomorphic encryption that leads to the privacy of patient data against hacking.


Law Enforcement:  Homomorphic encryption can assist regulation enforcement and use predictive evaluation to locate crime through virtual evaluation. Law enforcement could most effectively be capable of getting admission to predictions of the model, in place of the complete facts set, to shield citizens` privacy.

Education: In education, instructors and directors can expect to threaten college students and interfere whilst respecting college students` privacy. They also can acquire information approximately why college students drop out. This calls for integrating information throughout exclusive institutions

Limitation of homomorphic encryption 

Multiple user support: let’s say there are multiple users using the same account and who wish to protect their personal data from service providers, the solution to this problem from the provider is to provide separate data for every user who is using the same account. But in the case of a large database, it will be impossible to provide separate data and may lead to compromise.

Large Application:  Fully homomorphic encryption algorithms have large computational power, which shows the ratio of computation time in the encrypted version to that in the plain text. So it is not practically possible to handle large-scale applications in this encryption technique.

Conclusion 

Homomorphic encryption is a technological surprise that guarantees revolutionizing facts, privateness, and constant computation. Its functionality packages are vast, from strong outsourcing to privateness-preserving facts evaluation and collaborative device mastering. While it faces annoying situations, ongoing research and development regularly mitigate the boundaries.

As we navigate the facts-driven global, homomorphic encryption stands as a beacon of hope, providing the promise of privateness in technology of ubiquitous facts sharing and evaluation. It can be underrated nowadays, but it’s some distance an era with a super and transformative future.

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