17962843. Graph Based Embedding Under Fully Homomorphic Encryption simplified abstract (International Business Machines Corporation)

Graph Based Embedding Under Fully Homomorphic Encryption

Organization Name

International Business Machines Corporation

Inventor(s)

Allon Adir of Kiryat Tivon (IL)

Ramy Masalha of Kafr Qari (IL)

Eyal Kushnir of Kfar Vradim (IL)

OMRI Soceanu of Haifa (IL)

Ehud Aharoni of Kfar Saba (IL)

Nir Drucker of Zichron Yaakov (IL)

GUY Moshkowich of Nes Ziyona (IL)

Graph Based Embedding Under Fully Homomorphic Encryption - A simplified explanation of the abstract

This abstract first appeared for US patent application 17962843 titled 'Graph Based Embedding Under Fully Homomorphic Encryption

Simplified Explanation

The patent application describes mechanisms for fully homomorphic encryption enabled graph embedding, where encrypted entities and predicates are received and used to generate corresponding ciphertexts based on initial embeddings of features. A machine learning process iteratively updates these embeddings to generate a computer model for embedding entities and predicates, resulting in a final embedding output.

• Mechanisms for fully homomorphic encryption enabled graph embedding
• Generation of ciphertexts for encrypted entities and predicates based on initial embeddings
• Iterative machine learning process to update embeddings of features
• Generation of a computer model for embedding entities and predicates
• Output of a final embedding based on updated embeddings

Potential Applications

The technology described in the patent application could have potential applications in:

• Secure data sharing and analysis
• Privacy-preserving machine learning
• Encrypted graph analytics

Problems Solved

This technology addresses the following problems:

• Securely embedding graph data structures
• Preserving privacy of sensitive information in machine learning processes
• Enabling encrypted graph analytics without compromising data security

Benefits

The benefits of this technology include:

• Enhanced data security and privacy protection
• Facilitation of secure and private machine learning tasks
• Efficient encrypted graph analytics capabilities

Potential Commercial Applications

The technology could be commercially applied in:

• Secure data analytics platforms
• Privacy-preserving machine learning services
• Encrypted graph database systems

Possible Prior Art

One possible prior art in this field is the work on homomorphic encryption and secure multiparty computation for privacy-preserving data analysis.

How does this technology compare to existing methods for encrypted graph embedding?

This article does not provide a direct comparison with existing methods for encrypted graph embedding.

What are the computational requirements for implementing this technology in real-world applications?

The article does not address the specific computational requirements for implementing this technology in real-world applications.

Original Abstract Submitted

Mechanisms are provided for fully homomorphic encryption enabled graph embedding. An encrypted graph data structure, having encrypted entities and predicates, is received and, for each encrypted entity, a corresponding set of entity ciphertexts is generated based on an initial embedding of entity features. For each encrypted predicate, a corresponding predicate ciphertext is generated based on an initial embedding of predicate features. A machine learning process is iteratively executed, on the sets of entity ciphertexts and the predicate ciphertexts, to update embeddings of the entity features of the encrypted entities and update embeddings of predicate features of the encrypted predicates, to generate a computer model for embedding entities and predicates. A final embedding is output based on the updated embeddings of the entity features and predicate features of the computer model.