Understanding Homomorphic Encryption


Here in this article, we will discuss about Homomorphic Encryption. What is fully homomorphic encryption over the integers and homomorphic cryptography ? You also know in this article about homomorphic computing. You’re find in this article how helpful the differential privacy for encrypted data analysis. In this article we will discuss about homomorphic encryption meaning, homomorphic encryption benefits and examples . also you can check homomorphic encryption online platform.

Concern about privacy are at an all time high in the era of big data. It is essentiall to protect your data from unauthorised access and exploitation since sensitive information is being gathered and processed on such large scale. When it’s comes to performing calculations on encrypted data without first decoding it homomorphic encryption is useful.

Let’s start with homomorphic encryption meaning :

A type of homomorphic cryptography known as a homomorphic encryption allow computations to be done on encrypted data. This allows the storage of your sensitive data without ever having to decrypt it providing a higher level of security than is possible with conventional cryptographic techniques. homomorphic encryption machine learning that reduces the need to first homomorphic encryption decoder encrypted data  in order for analysts and data scientists to perform analytical functions on it. homomorphic encryption in cloud computing for securing sensitive data while it is being handle by outside cloud providers. It allows encryption of data while it is stored by cloud server lower the possibility of data leaks and cyberattacks.

There are 4 types of Homomorphic Encryption :

  1. Partially Homomorphic Encryption (PHE)
  2. Somewhat Homomorphic Encryption (SHE)
  3. Fully Homomorphic Encryption (FHE)
  4. Quantum Homomorphic Encryption (QHE)

Partially Homomorphic Encryption

Partially Homomorphic Encryption is a homomorphic encryption method that only peremits computations on data that has been encrypted or has been converted to plaintext . This makes it less effective than somewhat homomorphic encryption but more effective and simpler to use.

Somewhat Homomorphic Encryption

The development of fairly realistic Fullly Homomorphic Encryptionsystems as Palisade homomorphic encryption which employ more effective cryptographic algorithms to lower the computing overhead has advanced recently. Somewhat real u sers can do calculations on encrypted data using the somewhat practical fully homomorphic encryption kind of encryption technique without first needing to decrypt it.

Fully Homomorphic Encryption

Partially Homomorphic Encryption is a homomorphic encryption technique that enables computations to be made on encrypte material without ever having to decrypt it. As a result only the holder of the cryptographic keys can decrypt the computation results which are also encrypted.

Quantum homomorphic encryption

Quantum homomorphic encryption  is  encryption method that allows quantum computing to be carried out on private data belong to one party using a program provided by a different party without disclosing much about the data or the programme to the other party.

Homomorphic encryption blockchain

Data processing without decryption is made posible by homomorphic encryption . On top of open permissionless blockchains this can be used to build private smart contracts . where only selected users can view the transactional information and contract states.

Homomorphic encryption advantages and disadvantages

Homomorphic encryption advantages :

  • Privacy
  • Flexibility
  • Security
  • More efficiency

Homomorphic encryption disadvantages :

  • More Complex
  • Execution
  • Key management
  • Costly

Homomorphic encryption example

The situation where a data owner wishe to tranfer data to the cloud for processing but does not trust a service provider with theier data is the most common case for the use of homomorphic encryption. The data owner encrypts their data using a homomorphic encryption algorithm before sending it to the server.

Suppose you have a two numbers first is 2 and second is 3 and you want to calculate sum . you would to first encrypt each number using specific homomorphic encryption algorithm.

In homomorphic encryption python is a popular language for data analysis and machine learning there are many python homomorphic encryption library like Pyfhel, HElib and TenSEAL .

Privacy-preserving computation

In order to preserve sensitive data and provide secure computation over encrypted data, privacy-preserving computation algorithms have been proposed. data cannot be gatherred or misused since it is encrypted during calculation.

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