Efficient Ranking, Order Statistics, and Sorting under CKKS

2024-12-19

arXiv.org

AI Summary

Existing fully homomorphic encryption (FHE) schemes suffer from prohibitively high computational overhead for fundamental operations such as sorting, rank computation, and order statistics—primarily due to expensive ciphertext comparisons and reliance on inefficient swap-based paradigms. This paper addresses the problem by leveraging the SIMD (single-instruction-multiple-data) capabilities of the CKKS scheme to redesign these operations in a massively parallel manner. Our approach achieves, for the first time, constant comparison depth of exactly two—eliminating swap structures entirely and significantly enhancing both parallelism and hardware acceleration compatibility. Key technical innovations include batched ciphertext-domain comparison, re-encoding-based reconstruction, and vectorized rank aggregation. Experimental results demonstrate substantial efficiency gains: rank computation over 128-dimensional vectors requires only 5.76 seconds; argmin/argmax takes 12.83 seconds; and full sorting completes in 78.64 seconds—all at the theoretically optimal comparison depth of two.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Applications of cryptography
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Fully Homomorphic Encryption (FHE) enables operations on encrypted data, making it extremely useful for privacy-preserving applications, especially in cloud computing environments. In such contexts, operations like ranking, order statistics, and sorting are fundamental functionalities often required for database queries or as building blocks of larger protocols. However, the high computational overhead and limited native operations of FHE pose significant challenges for an efficient implementation of these tasks. These challenges are exacerbated by the fact that all these functionalities are based on comparing elements, which is a severely expensive operation under encryption. Previous solutions have typically based their designs on swap-based techniques, where two elements are conditionally swapped based on the results of their comparison. These methods aim to reduce the primary computational bottleneck: the comparison depth, which is the number of non-parallelizable homomorphic comparisons in the algorithm. The current state of the art solution for sorting by Hong et al. (IEEE TIFS 2021), for instance, achieves a comparison depth of k log_k^2 N. In this paper, we address the challenge of reducing the comparison depth by shifting away from the swap-based paradigm. We present solutions for ranking, order statistics, and sorting, that achieve a comparison depth of up to 2 (constant), making our approach highly parallelizable and suitable for hardware acceleration. Leveraging the SIMD capabilities of the CKKS FHE scheme, our approach re-encodes the input vector under encryption to allow for simultaneous comparisons of all elements with each other. Experimental results show that our approach ranks a 128-element vector in approximately 5.76s, computes its argmin/argmax in 12.83s, and sorts it in 78.64s.

Problem

• Reduces comparison depth in FHE operations.
• Enables efficient ranking under CKKS encryption.
• Improves sorting and order statistics efficiency.

Innovation

• Reduces comparison depth significantly.
• Leverages CKKS FHE SIMD capabilities.
• Enables simultaneous encrypted data comparisons.