Homomorphic Encryption Vs. Multiparty Computation

Bleeding edge encryption techniques allow one to monetize their personal health information without sacrificing privacy.


Encryption is the conversion of information into a code unreadable for unauthorized users by the use of an algorithm. Encryption is important when you don’t want anyone else to have access to your private data, such as brainwave data, selfie video data, or personal health data. There are many ways to compute (do math on) the encrypted data without knowing whose information it is about out of which, two are: Homomorphic Encryption and Multiparty Computation.

Homomorphic encryption is a kind of encryption in which the data is converted into a ciphertext which can be later analysed and worked on as it was still in its original form. The ciphertext is an encrypted version of the input data. It is operated on and then decrypted to obtain the desired output. This encryption allows us to perform complex mathematical operations on encrypted and secured data. It transforms one data set into another without harming the relationship between elements of both sets.

Multiparty computation is used to evaluate the inputs of two or more parties while keeping their inputs hidden from each other. This is done when different parties wish to jointly compute a function to their inputs in such a way that there certain security properties are preserved.

In simpler term, encryption allows us to hide data in a way that appears meaningless to anyone except those who have access to the secret decryption key. 



There have been many attempts to secure genomic privacy of biologically researched data using cryptographic methods. Particularly, it has been suggested that the privacy can be protected through homomorphic encryption.

The math on brainwave data recorded, of secret participants, using EEG while watching TV commercials, can be done through homomorphic encryption without decrypting the data.

The companies that get the brainwave data, never want to reveal the identity of their participants, that is why they send the samples in an encrypted form, to the lab, where the computations are done using homomorphic encryption, and the predictions (results) are sent back, to the company, in the encrypted form; where only they can decrypt it back using decryption keys. In this way, the identity of the person is never disclosed. The data is encrypted, also because companies and labs are bound by regulations and participant’s agreements to handle his data confidentially.


Multiparty computation can be implemented using different protocols, such as Secret Sharing, in which the data from each party is divided and computed on separately. Then after combining again, it provides the desired statistical results. Security in multiparty computation means that the players’ inputs remain secured (except for the results that are computed) and the results computed are correctly. The security is supposed to be preserved In the face of any sort of adversary. Intuitively, no party learns about any other party’s inputs.

All in all, computation of encrypted data is an interesting topic that explains how cryptography faces the hardest problem of protecting data in use. This is just an overall review about what these two methods of computation have to offer us. The past few years have seen the most significant advances in making the use of these two technologies on more wider-scale.


For more information, see the project at OpenMined.org




BUILDING SAFE ARTIFICIAL INTELLIGENCE. (n.d.). Retrieved from open mined: https://www.openmined.org/

Computing Over Encrypted Data. (2017, may 29). Retrieved from Enigma: https://blog.enigma.co/computing-over-encrypted-data-d36621458447

homomorphic encryption. (n.d.). Retrieved from Search Security: https://searchsecurity.techtarget.com/definition/homomorphic-encryption

Homomorphic Encryption Market Size, Worldwide Analysis, Design Competition Strategies, Company Profile, Development Status, Opportunity Assessment and Industry Expansion Strategies 2027. (2018, june 7). Retrieved from 14 News: http://www.14news.com/story/38373250/homomorphic-encryption-market-size-worldwide-analysis-design-competition-strategies-company-profile-development-status-opportunity-assessment-and

Private predictive analysis on encrypted medical data. (2014, Aug). Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S1532046414000884

Secure multi-party computation made simple. (2006, Feb 1). Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S0166218X05002428



2 thoughts on “Homomorphic Encryption Vs. Multiparty Computation”

  1. its an awesome post and nice course, here nice details about bleeding edge encryption techniques allow one to monetize their personal health information without sacrificing privacy. its good for all people

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