International business machines corporation (20240137205). EFFICIENT RANDOM MASKING OF VALUES WHILE MAINTAINING THEIR SIGN UNDER FULLY HOMOMORPHIC ENCRYPTION (FHE) simplified abstract

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EFFICIENT RANDOM MASKING OF VALUES WHILE MAINTAINING THEIR SIGN UNDER FULLY HOMOMORPHIC ENCRYPTION (FHE)

Organization Name

international business machines corporation

Inventor(s)

Allon Adir of Kiryat Tivon (IL)

Ramy Masalha of Kafr Qari (IL)

Ehud Aharoni of Kfar Saba (IL)

EFFICIENT RANDOM MASKING OF VALUES WHILE MAINTAINING THEIR SIGN UNDER FULLY HOMOMORPHIC ENCRYPTION (FHE) - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240137205 titled 'EFFICIENT RANDOM MASKING OF VALUES WHILE MAINTAINING THEIR SIGN UNDER FULLY HOMOMORPHIC ENCRYPTION (FHE)

Simplified Explanation

The patent application describes a method, apparatus, and computer program product for privacy-preserving homomorphic inferencing. In simple terms, this technology allows for performing calculations on encrypted data without decrypting it.

  • Generation of ciphertext of real numbers with associated signs
  • Identification of a mask with randomly distributed values over a positive range
  • Multiplication of the ciphertext by the mask under homomorphic encryption
  • Resulting values maintain their associated signs and are provided as a response to the encrypted data

Potential Applications

This technology can be applied in secure data analysis, confidential computing, and privacy-preserving machine learning.

Problems Solved

1. Protecting sensitive data during computation 2. Enabling secure outsourcing of data analysis tasks

Benefits

1. Enhanced privacy protection 2. Secure data processing without compromising confidentiality

Potential Commercial Applications

  • Secure data analytics platforms
  • Confidential machine learning services

Possible Prior Art

There are existing methods for homomorphic encryption and secure computation, but this specific approach of using masks with fixed-point arithmetic and low scale values may be novel.

What are the limitations of this technology in real-world applications?

This technology may introduce computational overhead due to the encryption and decryption processes involved. Additionally, the complexity of the iterative algorithm used to identify the mask may impact the efficiency of the system.

How does this technology compare to existing methods of privacy-preserving computation?

This technology offers a unique approach by combining homomorphic encryption with the use of masks to maintain the privacy of real numbers with associated signs. Existing methods may focus on either encryption or masking techniques separately, making this innovation a potentially comprehensive solution for privacy-preserving inferencing.


Original Abstract Submitted

a method, apparatus and computer program product for privacy-preserving homomorphic inferencing. in response to receipt of encrypted data, a ciphertext of real numbers is generated. each real number has an associated sign that is desired to be maintained. a mask is then identified, preferably via an iterative algorithm that works on a trial and error basis to locate an appropriate solution. the mask comprises set of values randomly distributed over a given positive range and that remain positive after encoding under a fixed-point arithmetic and with a low scale value. under homomorphic encryption, the ciphertext is then multiplied by the mask to generate a result comprising values corresponding to the real numbers in the ciphertext and that maintain their associated signs. the result is provided as a response to the encrypted data.

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