MapComp: A Secure View-based Collaborative Analytics Framework for Join-Group-Aggregation
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912673002684416 |
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| author | Peng, Xinyu Han, Feng Peng, Li Liu, Weiran Yan, Zheng Kang, Kai Zhang, Xinyuan Wei, Guoxing Sun, Jianling Liu, Jinfei Qu, Lin |
| author_facet | Peng, Xinyu Han, Feng Peng, Li Liu, Weiran Yan, Zheng Kang, Kai Zhang, Xinyuan Wei, Guoxing Sun, Jianling Liu, Jinfei Qu, Lin |
| contents | Join-group-aggregation (JGA) queries are fundamental to data analytics, yet executing them collaboratively across different parties poses significant privacy risks. Secure multi-party computation (MPC) offers a cryptographic solution. However, existing MPC-based JGA approaches consider only a one-time query paradigm and suffer from significant performance bottlenecks. It executes expensive join operations from scratch across multiple queries and employs inefficient group-aggregation (GA) protocols, both of which hinder their practical use for scalable, real-time analysis.
This paper introduces MapComp, a novel view-based framework to facilitate JGA queries for secure collaborative analytics. Through specially crafted materialized views for join and novel design of GA protocols, MapComp removes duplicate join workload and expedites subsequent GA, improving the efficiency of JGA query execution. To address the challenge of continuous data updates, our materialized view offers payload-independence feature and provides significant efficiency improvements in view refreshing with free MPC overhead. This feature, on the other hand, also allows further acceleration for GA, where we devise multiple novel protocols that outperform prior works. Notably, our work represents the first endeavor to expedite secure collaborative JGA queries using materialized views. Our rigorous experiments demonstrate a significant advantage of MapComp, achieving up to a 308.9x improvement in efficiency over the baseline in real-world query simulations. Moreover, our optimized GA protocols achieve up to a 1140.5x improvement compared to prior oblivious sorting-based solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MapComp: A Secure View-based Collaborative Analytics Framework for Join-Group-Aggregation Peng, Xinyu Han, Feng Peng, Li Liu, Weiran Yan, Zheng Kang, Kai Zhang, Xinyuan Wei, Guoxing Sun, Jianling Liu, Jinfei Qu, Lin Cryptography and Security Join-group-aggregation (JGA) queries are fundamental to data analytics, yet executing them collaboratively across different parties poses significant privacy risks. Secure multi-party computation (MPC) offers a cryptographic solution. However, existing MPC-based JGA approaches consider only a one-time query paradigm and suffer from significant performance bottlenecks. It executes expensive join operations from scratch across multiple queries and employs inefficient group-aggregation (GA) protocols, both of which hinder their practical use for scalable, real-time analysis. This paper introduces MapComp, a novel view-based framework to facilitate JGA queries for secure collaborative analytics. Through specially crafted materialized views for join and novel design of GA protocols, MapComp removes duplicate join workload and expedites subsequent GA, improving the efficiency of JGA query execution. To address the challenge of continuous data updates, our materialized view offers payload-independence feature and provides significant efficiency improvements in view refreshing with free MPC overhead. This feature, on the other hand, also allows further acceleration for GA, where we devise multiple novel protocols that outperform prior works. Notably, our work represents the first endeavor to expedite secure collaborative JGA queries using materialized views. Our rigorous experiments demonstrate a significant advantage of MapComp, achieving up to a 308.9x improvement in efficiency over the baseline in real-world query simulations. Moreover, our optimized GA protocols achieve up to a 1140.5x improvement compared to prior oblivious sorting-based solutions. |
| title | MapComp: A Secure View-based Collaborative Analytics Framework for Join-Group-Aggregation |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2408.01246 |