What Is Homomorphic Encryption & How Is It Used | Venafi

What is Homomorphic Encryption?

The function of homomorphic encoding is to allow calculation on encrypted data. Thus datum can remain confidential while it is processed, enabling utilitarian tasks to be accomplished with data residing in untrusted environments. In a worldly concern of distribute calculation and heterogenous network this is a enormously valuable capability .
A homomorphic cryptosystem is like other forms of populace encoding in that it uses a public key to encrypt data and allows only the individual with the matching secret winder to access its unencrypted data. however, what sets it apart from early forms of encoding is that it uses an algebraic organization to allow you or others to perform a variety show of computations ( or operations ) on the encrypted datum .

In mathematics, homomorphic describes the transformation of one data set into another while preserving relationships between elements in both sets. The term is derived from the greek words for “ same structure. ” Because the data in a homomorphic encoding outline retains the lapp structure, identical mathematical operations, whether they are performed on code or decrypted data, will result in equivalent results .
In commit, most homomorphic encoding schemes work well with data represented as integers and while using summation and multiplication as the operational functions. This means that the encrypted data can be manipulated and analyzed as though it ’ randomness in plaintext format without actually being decrypted. They can compute and process the encrypted data to get an code answer, but lone you can decrypt the ciphertext and understand what it means. Homomorphic encoding requires few rounds of interactions and uses arithmetic functions which focus on additions and multiplication, preferably than boolean functions like other methods of plug calculation.

Types of Homomorphic Encryption
There are three types of homomorphic encoding. The primary difference between them is related to the types and frequency of numerical operations that can be performed on the ciphertext. The three types are :

  • Partially Homomorphic Encryption

  • Somewhat Homomorphic Encryption

  • Fully Homomorphic Encryption


partially homomorphic encoding ( PHE ) allows only choose numerical functions to be performed on code values. This means that only one operation, either accession or generation, can be performed an outright number of times on the ciphertext. PHE with multiplicative operations is the foundation for RSA encoding, which is normally used in establishing secure connections through SSL/TLS .
A reasonably homomorphic encoding ( SHE ) dodge is one that supports choose operation ( either addition or multiplication ) up to a certain complexity, but these operations can alone be performed a set numeral of times .

Fully Homomorphic Encryption

amply homomorphic encoding ( FHE ), while still in the development phase, has a fortune of potential for making functionality reproducible 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 summation and multiplication any number of times and makes procure multi-party calculation more efficient. Unlike other forms of homomorphic encoding, it can handle arbitrary computations on your ciphertexts .
The goal behind FHE is to allow anyone to use code data to perform utilitarian operations without access to the encoding key. In particular, this concept has applications for improving obscure computing security. If you want to store code, sensitive data in the obscure but wear ’ deoxythymidine monophosphate want to run the risk of a hacker breaking in your defile account, it provides you with a room to pull, research, and manipulate your data without having to allow the cloud supplier access to your data .

Security of Fully Homomorphic Encryption

The security of the homomorphic encoding schemes is based on the Ring-Learning With Errors ( RLWE ) trouble, which is a unvoiced mathematical problem related to high-dimensional lattices. A bang-up number of peer-reviewed research confirming the unfeelingness of the RLWE trouble gives us confidence that these schemes are indeed at least american samoa plug as any standardized encoding system.
In addition, RLWE and, subsequently, most homomorphic encoding schemes are considered to be secure against quantum computers, making them in fact more secure than factorization and discrete logarithm-based systems such as RSA and many forms of elliptic swerve cryptography. In fact, the post-quantum cryptography standardization project organized by NIST had several submissions based on difficult wicket problems exchangeable to what modern homomorphic encoding uses.

Applications of Fully Homomorphic Encryption

Craig Gentry mentioned in his graduation thesis that “ Fully homomorphic encoding has numerous applications. For model, it enables private queries to a search engine—the drug user submits an code question and the search engine computes a compendious code answer without ever looking at the question in the well-defined. It besides enables searching on encrypted data—a drug user stores encrypted files on a remote control file server and can late have the server retrieve alone files that ( when decrypted ) satisfy some Boolean restraint, even though the waiter can not decrypt the files on its own. More broadly, amply homomorphic encoding improves the efficiency of fasten multi party calculation. ”
Researchers have already identified respective practical applications of FHE, some of which are discussed herein :

  • Securing Data Stored in the Cloud

    . Using homomorphic encryption, you can secure the data that you store in the cloud while also retaining the ability to calculate and search ciphered information that you can later decrypt without compromising the integrity of the data as a whole.


  • Enabling Data Analytics in Regulated Industries

    . Homomorphic encryption allows data to be encrypted and outsourced to commercial cloud environments for research and data-sharing purposes while protecting user or patient data privacy. It can be used for businesses and organizations across a variety of industries including financial services, retail, information technology, and healthcare to allow people to use data without seeing its unencrypted values. Examples include

    predictive analysis of medical data

    without putting data privacy at risk,

    preserving customer privacy

    in personalized advertising, financial privacy for functions like

    stock price prediction algorithms

    , and forensic image recognition.


  • Improving Election Security and Transparency

    . Researchers are working on

    how to use homomorphic encryption to make democratic elections more secure and transparent

    . For example, the Paillier encryption scheme, which uses addition operations, would be best suited for voting-related applications because it allows users to add up various values in an unbiased way while keeping their values private. This technology could not only protect data from manipulation, it could allow it to be independently verified by authorized third parties.

Limitations of Fully Homomorphic Encryption

There are presently two know limitations of FHE. The beginning limitation is support for multiple users. Suppose there are many users of the same organization ( which relies on an internal database that is used in computations ), and who wish to protect their personal data from the provider. One solution would be for the provider to have a separate database for every drug user, encrypted under that user ’ s public key. If this database is very big and there are many users, this would cursorily become impracticable .
following, there are limitations for applications that involve running very large and complex algorithm homomorphically. All fully homomorphic encoding schemes today have a large computational overhead, which describes the proportion of calculation time in the code adaptation versus calculation time in the clear. Although polynomial in size, this disk overhead tends to be a rather large polynomial, which increases runtimes substantially and makes homomorphic calculation of complex functions airy .

Implementations of Fully Homomorphic Encryption

Some of the world ’ s largest engineering companies have initiated programs to advance homomorphic encoding to make it more universally available and user-friendly .
Microsoft, for exemplify, has created SEAL ( Simple Encrypted Arithmetic Library ), a laid of encoding libraries that allow computations to be performed immediately on encrypted data. Powered by open-source homomorphic encoding engineering, Microsoft ’ second SEAL team is partnering with companies like IXUP to build throughout encrypted data storage and calculation services. Companies can use SEAL to create platforms to perform data analytics on information while it ’ mho still encrypted, and the owners of the data never have to share their encoding key with anyone else. The goal, Microsoft says, is to “ put our library in the hands of every developer, so we can work together for more fasten, secret, and trustworthy calculate. ”
Google besides announced its back for homomorphic encoding by unveiling its open-source cryptanalytic tool, Private Join and Compute. Google ’ mho cock is focused on analyzing data in its code mannequin, with only the insights derived from the analysis visible, and not the underlying data itself.
finally, with the goal of making homomorphic encoding far-flung, IBM released its first version of its HElib C++ library in 2016, but it reportedly “ ran 100 trillion times slower than plaintext operations. ” Since that time, IBM has continued working to combat this exit and have come up with a interpretation that is 75 times faster, but it is still lagging behind plaintext operations.


In an era when the focus on privacy has increased, largely because of regulations such as GDPR, the concept of homomorphic encoding is one with a lot of promise for real-world applications across a kind of industries. And possibly one of the most stimulate aspects is how it combines the indigence to protect privacy with the indigence to provide more detail analysis. Homomorphic encoding has transformed an Achilles heel into a gift from the gods.

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