DeepAI AI Chat
Log In Sign Up

Rache: Radix-additive caching for homomorphic encryption

by   Dongfang Zhao, et al.
University of Nevada, Reno

One of the biggest concerns for many applications in cloud computing lies in data privacy. A potential solution to this problem is homomorphic encryption (HE), which supports certain operations directly over the ciphertexts. Conventional HE schemes, however, exhibit significant performance overhead and are hardly applicable to real-world applications. This paper presents Rache, a caching optimization for accelerating the performance of HE schemes. The key insights of Rache include (i) caching some homomorphic ciphertexts before encrypting the large volume of plaintexts; (ii) expanding the plaintexts into a summation of powers of radixes; and (iii) constructing the ciphertexts with only homomorphic addition. The extensive evaluation shows that Rache exhibits almost linear scalability and outperforms Paillier by orders of magnitude.


page 1

page 2

page 3

page 4


Silca: Singular Caching of Homomorphic Encryption for Outsourced Databases in Cloud Computing

Ensuring the confidentiality and privacy of sensitive information in clo...

High-Performance Caching of Homomorphic Encryption for Cloud Databases

While homomorphic encryption (HE) has garnered significant research inte...

A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Performing smart computations in a context of cloud computing and big da...

Sealer: In-SRAM AES for High-Performance and Low-Overhead Memory Encryption

To provide data and code confidentiality and reduce the risk of informat...

INCHE: High-Performance Encoding for Relational Databases through Incrementally Homomorphic Encryption

Homomorphic encryption (HE) offers data confidentiality by executing que...

Supporting Secure Dynamic Alert Zones Using Searchable Encryption and Graph Embedding

Location-based alerts have gained increasing popularity in recent years,...

Helix: Holistic Optimization for Accelerating Iterative Machine Learning

Machine learning workflow development is a process of trial-and-error: d...