Efficient Encrypted Range Query on Cloud Platforms
Article No.: 27, Pages 1 - 23
Abstract
In the Internet of Things (IoT) era, various IoT devices are equipped with sensing capabilities and employed to support clinical applications. The massive electronic health records (EHRs) are expected to be stored in the cloud, where the data are usually encrypted, and the encrypted data can be used for disease diagnosis. There exist some numeric health indicators, such as blood pressure and heart rate. These numeric indicators can be classified into multiple ranges, and each range may represent an indication of normality or abnormity. Once receiving encrypted IoT data, the CS maps it to one of the ranges, achieving timely monitoring and diagnosis of health indicators. This article presents a new approach to identify the range that an encrypted numeric value corresponds to without exposing the explicit value. We establish the sufficient and necessary condition to convert a range query to matchings of encrypted binary sequences with the minimum number of matching operations. We further apply the minimization of range queries to design and implement a secure range query system, where numeric health indicators encrypted independently by multiple IoT devices can be cohesively stored and efficiently queried by using Lagrange polynomial interpolation. Comprehensive performance studies show that the proposed approach can protect both the health records and range query against untrusted cloud platforms and requires less computational and communication cost than existing techniques.
References
[1]
Li Da Xu, Wu He, and Shancang Li. 2014. Internet of Things in industries: A survey. IEEE Trans. Industr. Inform. 10, 4 (2014), 2233–2243.
[2]
Javier Andreu-Perez, Carmen C. Y. Poon, Robert D. Merrifield, Stephen T. C. Wong, and Guang-Zhong Yang. 2015. Big data for health. IEEE J. Biomed. Health Inform. 19, 4 (2015), 1193–1208.
[3]
Shaohua Wan, Yu Zhao, Tian Wang, Zonghua Gu, Qammer H. Abbasi, and Kim-Kwang Raymond Choo. 2019. Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things. Fut. Gen. Comput. Syst. 91 (2019), 382–391.
[4]
Ejaz Ahmed, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Imran Khan, Abdelmuttlib Ibrahim Abdalla Ahmed, Muhammad Imran, and Athanasios V. Vasilakos. 2017. The role of big data analytics in Internet of Things. Comput. Netw. 129 (2017), 459–471.
[5]
Richard Chow, Markus Jakobsson, Ryusuke Masuoka, Jesus Molina, Yuan Niu, Elaine Shi, and Zhexuan Song. 2010. Authentication in the clouds: A framework and its application to mobile users. In Proceedings of the ACM Workshop on Cloud Computing Security Workshop. ACM, 1–6.
[6]
Jiang Qian, Susan Hinrichs, and Klara Nahrstedt. 2001. ACLA: A framework for access control list (ACL) analysis and optimization. In Communications and Multimedia Security Issues of the New Century. Springer, 197–211.
[7]
Lei Xu, Shifeng Sun, Xingliang Yuan, Joseph K. Liu, Cong Zuo, and Chungen Xu. 2019. Enabling authorized encrypted search for multi-authority medical databases. IEEE Trans. Emerg. Topics Comput. 9, 1 (2019), 534–546. DOI:
[8]
Boyang Wang, Ming Li, and Haitao Wang. 2016. Geometric range search on encrypted spatial data. IEEE Trans. Inf. Forens. Secur. 11, 4 (2016), 704–719.
[9]
Dan Boneh and Brent Waters. 2007. Conjunctive, subset, and range queries on encrypted data. In Proceedings of the Theory of Cryptography Conference. Springer, 535–554.
[10]
Guowen Xu, Hongwei Li, Yuanshun Dai, Kan Yang, and Xiaodong Lin. 2019. Enabling efficient and geometric range query with access control over encrypted spatial data. IEEE Trans. Inf. Forens. Secur. 14, 4 (4 2019), 870–885. DOI:
[11]
Muhammad Naveed, Seny Kamara, and Charles V. Wright. 2015. Inference attacks on property-preserving encrypted databases. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. ACM, 644–655.
[12]
Wenhai Sun, Ning Zhang, Wenjing Lou, and Y. Thomas Hou. 2017. When gene meets cloud: Enabling scalable and efficient range query on encrypted genomic data. In Proceedings of the IEEE Conference on Computer Communications. IEEE, 1–9.
[13]
Rui Li, Alex X. Liu, Ann L. Wang, Bezawada Bruhadeshwar, and Bezawada Bruhadeshwar. 2016. Fast and scalable range query processing with strong privacy protection for cloud computing. IEEE/ACM Trans. Netw. 24, 4 (2016), 2305–2318.
[14]
Yinbin Miao, Ximeng Liu, Robert H. Deng, Hongjun Wu, Hongwei Li, Jiguo Li, and Dapeng Wu. 2018. Hybrid keyword-field search with efficient key management for industrial Internet of Things. IEEE Trans. Industr. Inform. 15, 6 (2018), 3206–3217.
[15]
Christoph Bösch, Pieter Hartel, Willem Jonker, and Andreas Peter. 2015. A survey of provably secure searchable encryption. ACM Comput. Surv. 47, 2 (2015), 18.
[16]
Anselme Tueno and Florian Kerschbaum. 2020. Efficient secure computation of order-preserving encryption. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. 193–207. DOI:
[17]
Zhao Chang, Dong Xie, Feifei Li, Jeff M. Phillips, and Rajeev Balasubramonian. 2021. Efficient oblivious query processing for range and kNN queries. IEEE Trans. Knowl. Data Eng. (2021), 1–1. DOI:
[18]
Gilad Asharov, T. H. Hubert Chan, Kartik Nayak, Rafael Pass, Ling Ren, and Elaine Shi. 2019. Locality-preserving oblivious RAM. In Annual International Conference on the Theory and Applications of Cryptographic Techniques. Springer, 214–243.
[19]
Anrin Chakraborti, Adam J. Aviv, Seung Geol Choi, Travis Mayberry, Daniel S. Roche, and Radu Sion. 2019. rORAM: Efficient range ORAM with O(log2 N) locality. Internet Society. DOI:
[20]
Joseph K. Liu, Cheng-Kang Chu, Sherman S. M. Chow, Xinyi Huang, Man Ho Au, and Jianying Zhou. 2015. Time-bound anonymous authentication for roaming networks. IEEE Trans. Inf. Forens. Secur. 10, 1 (2015), 178–189.
[21]
Kaiping Xue, Jianan Hong, Yingjie Xue, David S. L. Wei, Nenghai Yu, and Peilin Hong. 2017. CABE: A new comparable attribute-based encryption construction with 0-encoding and 1-encoding. IEEE Trans. Comput. 66, 9 (2017), 1491–1503.
[22]
Hakan Hacigümüş, Bala Iyer, Chen Li, and Sharad Mehrotra. 2002. Executing SQL over encrypted data in the database-service-provider model. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 216–227.
[23]
Bo Sheng and Qun Li. 2008. Verifiable privacy-preserving range query in two-tiered sensor networks. In Proceedings of the IEEE 27th Conference on Computer Communications. IEEE, 46–50.
[24]
Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Rongxing Lu, and Jian Weng. 2018. Efficient and privacy-preserving outsourced calculation of rational numbers. IEEE Trans. Depend. Sec. Comput. 15, 1 (2018), 27–39.
[25]
Hassan Mahdikhani, Rongxing Lu, Yandong Zheng, Jun Shao, and Ali A. Ghorbani. 2020. Achieving O(log3n) communication-efficient privacy-preserving range query in fog-based IoT. IEEE Internet Things J. 7, 6 (6 2020), 5220–5232. DOI:
[26]
Min Zhou, Yandong Zheng, Yunguo Guan, Limin Peng, and Rongxing Lu. 2021. Efficient and privacy-preserving range-max query in fog-based agricultural IoT. Peer-to-Peer Netw. Applic. 14, 4 (2021), 2156–2170.
[27]
Cheng Guo, Ruhan Zhuang, Yingmo Jie, Kim Kwang Raymond Choo, and Xinyu Tang. 2019. Secure range search over encrypted uncertain IoT outsourced data. IEEE Internet Things J. 6, 2 (4 2019), 1520–1529. DOI:
[28]
Jinwen Liang, Zheng Qin, Sheng Xiao, Jixin Zhang, Hui Yin, and Keqin Li. 2020. Privacy-preserving range query over multi-source electronic health records in public clouds. J. Parallel Distrib. Comput. 135 (1 2020), 127–139. DOI:
[29]
Wei Yang, Yangyang Geng, Lu Li, Xike Xie, and Liusheng Huang. 2020. Achieving secure and dynamic range queries over encrypted cloud data. IEEE Trans. Knowl. Data Eng. 34, 1 (2022), 107–121. DOI:
[30]
Nam-Su Jho, Ku-Young Chang, Dowon Hong, and Changho Seo. 2016. Symmetric searchable encryption with efficient range query using multi-layered linked chains. J. Supercomput. 72, 11 (2016), 4233–4246.
[31]
Cong Zuo, Shi-Feng Sun, Joseph K. Liu, Jun Shao, and Josef Pieprzyk. 2018. Dynamic searchable symmetric encryption schemes supporting range queries with forward (and backward) security. In Proceedings of the European Symposium on Research in Computer Security. Springer, 228–246.
[32]
Jianqiu Xu, Hua Lu, and Ralf Hartmut Güting. 2018. Range queries on multi-attribute trajectories. IEEE Trans. Knowl. Data Eng. 30, 6 (2018), 1206–1211.
[33]
Reza Curtmola, Juan Garay, Seny Kamara, and Rafail Ostrovsky. 2011. Searchable symmetric encryption: Improved definitions and efficient constructions. J. Comput. Secur. 19, 5 (2011), 895–934.
[34]
Mi Wen, Rongxing Lu, Kuan Zhang, Jingsheng Lei, Xiaohui Liang, and Xuemin Shen. 2013. PaRQ: A privacy-preserving range query scheme over encrypted metering data for smart grid. IEEE Trans. Emerg. Topics Comput. 1, 1 (2013), 178–191.
[35]
Rongxing Lu. 2018. A new communication-efficient privacy-preserving range query scheme in fog-enhanced IoT. IEEE Internet Things J. 6, 2 (2019), 2497–2505. DOI:
[36]
Jong Hwan Park. 2011. Efficient hidden vector encryption for conjunctive queries on encrypted data. IEEE Trans. Knowl. Data Eng. 23, 10 (2011), 1483–1497.
[37]
Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H Deng, Yinbin Miao, Dan Zhu, and Zhuoran Ma. 2020. Search me in the dark: Privacy-preserving boolean range query over encrypted spatial data. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. 2253–2262.
[38]
Hsiao-Ying Lin and Wen-Guey Tzeng. 2005. An efficient solution to the millionaires’ problem based on homomorphic encryption. In Proceedings of the International Conference on Applied Cryptography and Network Security. Springer, 456–466.
[39]
Yinbin Miao, Jianfeng Ma, Ximeng Liu, Xinghua Li, Zhiquan Liu, and Hui Li. 2018. Practical attribute-based multi-keyword search scheme in mobile crowdsourcing. IEEE Internet Things J. 5, 4 (2018), 3008–3018.
[40]
Ximeng Liu, Rongxing Lu, Jianfeng Ma, Le Chen, and Haiyong Bao. 2016. Efficient and privacy-preserving skyline computation framework across domains. Fut. Gen. Comput. Syst. 62 (2016), 161–174.
[41]
Paul Grubbs, Marie Sarah Lacharite, Brice Minaud, and Kenneth G. Paterson. 2019. Learning to reconstruct: Statistical learning theory and encrypted database attacks. In Proceedings of the IEEE Symposium on Security and Privacy. 1067–1083. DOI:
[42]
Jianting Ning, Geong Sen Poh, Xinyi Huang, Robert Deng, Shuwei Cao, and Ee Chien Chang. 2020. Update recovery attacks on encrypted database within two updates using range queries leakage. IEEE Trans. Depend. Sec. Comput. 19, 2 (2022), 1164–1180. DOI:
[43]
Evgenios M. Kornaropoulos, Charalampos Papamanthou, and Roberto Tamassia. 2020. The state of the uniform: Attacks on encrypted databases beyond the uniform query distribution. In Proceedings of the IEEE Symposium on Security and Privacy. 1223–1240. DOI:
[44]
Jianting Ning, Jia Xu, Kaitai Liang, Fan Zhang, and Ee-Chien Chang. 2019. Passive attacks against searchable encryption. IEEE Trans. Inf. Forens. Secur. 14, 3 (2019), 789–802.
[45]
Yanguo Peng, Long Wang, Jiangtao Cui, Ximeng Liu, Hui Li, and Jianfeng Ma. 2020. LS-RQ: A lightweight and forward-secure range query on geographically encrypted data. IEEE Trans. Depend. Sec. Comput. 19, 1 (2022), 388–401. DOI:
[46]
Kee Sung Kim, Minkyu Kim, Dongsoo Lee, Je Hong Park, and Woo Hwan Kim. 2017. Forward secure dynamic searchable symmetric encryption with eicient updates. In Proceedings of the ACM Conference on Computer and Communications Security. 1449–1463. DOI:
[47]
Shi Feng Sun, Xingliang Yuan, Joseph K Liu, Ron Steinfeld, Amin Sakzad, Viet Vo, and Surya Nepal. Practical backwardsecure searchable encryption from symmetric puncturable encryption. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 763–780.
[48]
Raphael Bost. 2016. Forward secure searchable encryption. In Proceedings of the ACM Conference on Computer and Communications Security. 1143–1154. DOI:
[49]
Raphaël Bost, Brice Minaud, and Olga Ohrimenko. 2017. Forward and backward private searchable encryption from constrained cryptographic primitives. In Proceedings of the ACM Conference on Computer and Communications Security. 1465–1482. DOI:
[50]
Javad Ghareh Chamani, Dimitrios Papadopoulos, Charalampos Papamanthou, and Rasool Jalili. 2018. New constructions for forward and backward private symmetric searchable encryption. In Proceedings of the ACM Conference on Computer and Communications Security. 1038–1055. DOI:
[51]
Ghous Amjad, Seny Kamara, and Tarik Moataz. 2019. Forward and backward private searchable encryption with SGX. In Proceedings of the 12th European Workshop on Systems Security. DOI:
[52]
Longxia Huang, Junlong Zhou, Gongxuan Zhang, Jin Sun, Tongquan Wei, Shui Yu, and Shiyan Hu. 2020. IPANM: Incentive public auditing scheme for non-manager groups in clouds. IEEE Trans. Depend. Sec. Comput. 19, 2 (2022), 936–952. DOI:
Index Terms
- Efficient Encrypted Range Query on Cloud Platforms
Recommendations
Symmetric searchable encryption with efficient range query using multi-layered linked chains
Searchable encryption is an encryption system which provides confidentiality of stored documents and usability of document search at the same time. Remote cloud storage is the most typical application for searchable encryption. By applying searchable ...
Efficient and secure multi-dimensional geometric range query over encrypted data in cloud
AbstractSecure geometric range query, which aims to retrieve data points within a given geometric range from an encrypted dataset in the cloud, attracts more and more attention due to its wide applications. Up to now, several secure geometric ...
Highlights- We propose an attack to break the state-of-the-art secure range query scheme.
- ...
Efficient Privacy-Preserving Range Queries over Encrypted Data in Cloud Computing
CLOUD '13: Proceedings of the 2013 IEEE Sixth International Conference on Cloud ComputingWith the growing popularity of data and service outsourcing, where the data resides on remote servers in encrypted form, there remain open questions about what kind of query operations can be performed on the encrypted data. In this paper, we focus on ...
Comments
Information & Contributors
Information
Published In
July 2022
251 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Journal Family
Publication History
Published: 07 September 2022
Online AM: 19 July 2022
Accepted: 01 June 2022
Revised: 01 March 2022
Received: 01 October 2020
Published in TCPS Volume 6, Issue 3
Check for updates
Author Tags
Qualifiers
- Research-article
- Refereed
Funding Sources
- NSFC
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 415Total Downloads
- Downloads (Last 12 months)111
- Downloads (Last 6 weeks)3
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderFull Text
View this article in Full Text.
Full TextHTML Format
View this article in HTML Format.
HTML Format