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Journal articles on the topic 'Keyword-based search'

Author: Grafiati

Published: 10 January 2023

Last updated: 28 January 2023

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1

Kurhekar, Miss Rachana V., and Prof R. R. Shelke. "Location Based Nearest Keyword Search." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 1617–23. http://dx.doi.org/10.31142/ijtsrd8296.

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2

Seethalakshmi, G., and J. Swathi. "XML based Keyword Search." International Journal of Computer Applications 107, no. 15 (December 18, 2014): 1–3. http://dx.doi.org/10.5120/18824-0241.

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3

Yang Chen, Yang Chen, Yang Liu Yang Chen, Jin Pan Yang Liu, Fei Gao Jin Pan, and Emmanouil Panaousis Fei Gao. "Privacy-Protecting Attribute-Based Conjunctive Keyword Search Scheme in Cloud Storage." 網際網路技術學刊 24, no. 1 (January 2023): 065–75. http://dx.doi.org/10.53106/160792642023012401007.

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<p>Cloud storage has been deployed in various real-world applications. But how to enable Internet users to search over encrypted data and to enable data owners to perform fine-grained search authorization are of huge challenge. Attribute-based keyword search (ABKS) is a well-studied solution to the challenge, but there are some drawbacks that prevent its practical adoption in cloud storage context. First, the access policy in the index and the attribute set in the trapdoor are both in plaintext, they are likely to reveal the privacy of data owners and users. Second, the current ABKS schemes cannot provide multi-keyword search under the premise of ensuring security and efficiency. We explore an efficient way to connect the inner product encryption with the access control mechanism and search process in ABKS, and propose a privacy-protecting attribute-based conjunctive keyword search scheme. The proposed scheme provides conjunctive keyword search and ensures that the access policy and attribute set are both fully hidden. Formal security models are defined and the scheme is proved IND-CKA, IND-OKGA, access policy hiding and attribute set hiding. Finally, empirical simulations are carried out on real-world dataset, and the results demonstrate that our design outperforms other existing schemes in security and efficiency.</p> <p> </p>

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4

Hristidis, Vagelis, Heasoo Hwang, and Yannis Papakonstantinou. "Authority-based keyword search in databases." ACM Transactions on Database Systems 33, no. 1 (March 2008): 1–40. http://dx.doi.org/10.1145/1331904.1331905.

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5

Murali, Pranav. "An Approach to Trie Based Keyword Search for Search Engines." International Journal of Library and Information Services 6, no. 1 (January 2017): 1–16. http://dx.doi.org/10.4018/ijlis.2017010101.

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Search Engines use indexing techniques to minimize the time taken to find the relevant information to a search query. They maintain a keywords list that may reside either in the memory or in the external storage, like a hard disk. While a pure binary search can be used for this purpose, it suffers from performance issue when keywords are stored in the external storage. Some implementations of search engines use a B-tree and sparse indexes to reduce access time. This paper aims at reducing the keyword access time further. It presents a keyword search technique that utilizes a combination of trie data structure and a new keyword prefixing method. Experimental results show good improvement in performance over pure binary search. The merits of incorporating trie based approach into contemporary indexing methods is also discussed. Keyword prefixing method is described and some salient steps in the process of keyword generation are outlined.

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6

Bou, Savong, Toshiyuki Amagasa, and Hiroyuki Kitagawa. "Path-based keyword search over XML streams." International Journal of Web Information Systems 11, no. 3 (August 17, 2015): 347–69. http://dx.doi.org/10.1108/ijwis-04-2015-0013.

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Purpose – In purpose of this paper is to propose a novel scheme to process XPath-based keyword search over Extensible Markup Language (XML) streams, where one can specify query keywords and XPath-based filtering conditions at the same time. Experimental results prove that our proposed scheme can efficiently and practically process XPath-based keyword search over XML streams. Design/methodology/approach – To allow XPath-based keyword search over XML streams, it was attempted to integrate YFilter (Diao et al., 2003) with CKStream (Hummel et al., 2011). More precisely, the nondeterministic finite automation (NFA) of YFilter is extended so that keyword matching at text nodes is supported. Next, the stack data structure is modified by integrating set of NFA states in YFilter with bitmaps generated from set of keyword queries in CKStream. Findings – Extensive experiments were conducted using both synthetic and real data set to show the effectiveness of the proposed method. The experimental results showed that the accuracy of the proposed method was better than the baseline method (CKStream), while it consumed less memory. Moreover, the proposed scheme showed good scalability with respect to the number of queries. Originality/value – Due to the rapid diffusion of XML streams, the demand for querying such information is also growing. In such a situation, the ability to query by combining XPath and keyword search is important, because it is easy to use, but powerful means to query XML streams. However, none of existing works has addressed this issue. This work is to cope with this problem by combining an existing XPath-based YFilter and a keyword-search-based CKStream for XML streams to enable XPath-based keyword search.

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7

Chen, Yang, Wenmin Li, Fei Gao, Kaitai Liang, Hua Zhang, and Qiaoyan Wen. "Practical Attribute-Based Conjunctive Keyword Search Scheme." Computer Journal 63, no. 8 (December 9, 2019): 1203–15. http://dx.doi.org/10.1093/comjnl/bxz140.

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Abstract To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation.

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8

Li, Jiguo, Min Wang, Yang Lu, Yichen Zhang, and Huaqun Wang. "ABKS-SKGA: Attribute-based keyword search secure against keyword guessing attack." Computer Standards & Interfaces 74 (February 2021): 103471. http://dx.doi.org/10.1016/j.csi.2020.103471.

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9

V. Kurhekar, Miss Rachana. "Analysis on Location Based Nearest Keyword Search." International Journal for Research in Applied Science and Engineering Technology V, no. II (February 28, 2017): 267–70. http://dx.doi.org/10.22214/ijraset.2017.2041.

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10

Lou, Ying, Feng Zhong, JinXiang Zhang, and Qian Li. "Semantic keyword search based on information entropy." Journal of Physics: Conference Series 1952, no. 4 (June 1, 2021): 042080. http://dx.doi.org/10.1088/1742-6596/1952/4/042080.

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11

He, Qiang, Rui Zhou, Xuyun Zhang, Yanchun Wang, Dayong Ye, Feifei Chen, John C. Grundy, and Yun Yang. "Keyword Search for Building Service-Based Systems." IEEE Transactions on Software Engineering 43, no. 7 (July 1, 2017): 658–74. http://dx.doi.org/10.1109/tse.2016.2624293.

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12

Cheng, Huanyu, Ming Zhong, and Jian Wang. "Diversified keyword search based web service composition." Journal of Systems and Software 163 (May 2020): 110540. http://dx.doi.org/10.1016/j.jss.2020.110540.

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13

Zeng, Jia-Hui, Jiu-Ming Huang, and Shu-Qiang Yang. "Top-k Keyword Search Over Graphs Based On Backward Search." ITM Web of Conferences 12 (2017): 01014. http://dx.doi.org/10.1051/itmconf/20171201014.

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14

Cui, Yuanbo, Fei Gao, Yijie Shi, Wei Yin, Emmanouil Panaousis, and Kaitai Liang. "An Efficient Attribute-Based Multi-Keyword Search Scheme in Encrypted Keyword Generation." IEEE Access 8 (2020): 99024–36. http://dx.doi.org/10.1109/access.2020.2996940.

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15

Shen, Ming Yan, Xin Li, and Xiang Fu Meng. "Research and Implementation of XML Keyword Search Algorithm Based on Semantic Relatives." Advanced Materials Research 267 (June 2011): 811–15. http://dx.doi.org/10.4028/www.scientific.net/amr.267.811.

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The XML keyword search has been used widely in the application of XML documents. Most of the XML keyword search approaches are based on the LCA (lowest common ancestor) or its variants, which usually leads to the un-ideal recall and precision. This paper presents a novel XML keyword search method which based on semantic relatives. The method fully considers the semantic characteristics of the XML document structure. Based on the stack, the algorithm is also presented to merge the semantic relative nodes containing the keyword as the results of XML keyword search. The results of experiments have been identified the efficient and efficiency of our method.

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16

Yoon, Hyundo, and Junbeom Hur. "SGX-Based Public Key Encryption with Keyword Search." Journal of Korean Institute of Communications and Information Sciences 46, no. 5 (May 31, 2021): 777–87. http://dx.doi.org/10.7840/kics.2021.46.5.777.

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17

Zhenfang Li, and Shiqun Tao. "A XML Keyword Search Algorithm Based on MapReduce." International Journal of Digital Content Technology and its Applications 6, no. 17 (September 30, 2012): 307–16. http://dx.doi.org/10.4156/jdcta.vol6.issue17.33.

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18

Preethi, G. B. Sai, P. Radhika Raju, and A. Ananda Rao. "Relevant Keyword Search for Building Service-Based System." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 109–14. http://dx.doi.org/10.26438/ijcse/v6i7.109114.

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19

Yang, Zhen, Hongao Zhang, Haiyang Yu, Zheng Li, Bocheng Zhu, and Richard O. Sinnott. "Attribute-Based Keyword Search over the Encrypted Blockchain." Computer Modeling in Engineering & Sciences 128, no. 1 (2021): 269–82. http://dx.doi.org/10.32604/cmes.2021.015210.

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20

Chen, Zijun, Xin Wang, and Wenyuan Liu. "Reverse keyword-based location search on road networks." GeoInformatica 26, no. 1 (November 12, 2021): 201–31. http://dx.doi.org/10.1007/s10707-021-00440-3.

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21

ZHENG, Hong-hui, and Hong GUO. "XML keyword search algorithm based on efficient LCA." Journal of Computer Applications 30, no. 3 (April 7, 2010): 825–30. http://dx.doi.org/10.3724/sp.j.1087.2010.00825.

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22

Myint Thein, Myint. "Efficient Schema Based Keyword Search in Relational Databases." International Journal of Computer Science, Engineering and Information Technology 2, no. 6 (December 31, 2012): 13–32. http://dx.doi.org/10.5121/ijcseit.2012.2602.

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23

Ren, Xunyi, and Shiyang Yan. "Keyword-based Ciphertext Search Algorithm under Cloud Storage." MATEC Web of Conferences 61 (2016): 03002. http://dx.doi.org/10.1051/matecconf/20166103002.

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24

Liu, Jia’nan, Junzuo Lai, and Xinyi Huang. "Dual trapdoor identity-based encryption with keyword search." Soft Computing 21, no. 10 (December 9, 2015): 2599–607. http://dx.doi.org/10.1007/s00500-015-1960-6.

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25

Nguyen, Hoang-Minh, Hong-Quang Nguyen, Khoi-Nguyen Tran, and Xuan-Vinh Vo. "GeTFIRST: ontology-based keyword search towards semantic disambiguation." International Journal of Web Information Systems 11, no. 4 (November 16, 2015): 442–67. http://dx.doi.org/10.1108/ijwis-06-2015-0019.

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Purpose – This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities. Design/methodology/approach – A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity. Findings – An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches. Originality/value – The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm.

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26

Yang, Younghyoo. "Semantic-based Keyword Search System over Relational Database." Journal of the Korea Society of Computer and Information 18, no. 12 (December 31, 2013): 91–101. http://dx.doi.org/10.9708/jksci.2013.18.12.091.

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27

Shin, Sangjin, Jihoon Ko, Sungkwang Eom, Minjae Song, Dong-Hoon Shin, and Kyong-Ho Lee. "Keyword-based mobile semantic search using mobile ontology." Journal of Information Science 41, no. 2 (December 22, 2014): 178–96. http://dx.doi.org/10.1177/0165551514560669.

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28

Shi, Yanfeng, Jiqiang Liu, Zhen Han, Qingji Zheng, Rui Zhang, and Shuo Qiu. "Attribute-Based Proxy Re-Encryption with Keyword Search." PLoS ONE 9, no. 12 (December 30, 2014): e116325. http://dx.doi.org/10.1371/journal.pone.0116325.

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29

Yan, Zhepeng, Nan Zheng, Zachary G. Ives, Partha Pratim Talukdar, and Cong Yu. "Active learning in keyword search-based data integration." VLDB Journal 24, no. 5 (January 8, 2015): 611–31. http://dx.doi.org/10.1007/s00778-014-0374-x.

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30

Prakash, Miss Pawar Pratima. "A Review on Enabling Synonym Based Fined-grained Multi-keyword Search Using Hierarchical Clustering." International journal of Emerging Trends in Science and Technology 03, no. 12 (December 20, 2016): 4866–70. http://dx.doi.org/10.18535/ijetst/v3i12.11.

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31

Yao, Quan Zhu, Bing Tian, and Wang Yun He. "XML Keyword Search Algorithm Based on Level-Traverse Encoding." Applied Mechanics and Materials 263-266 (December 2012): 1553–58. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1553.

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For XML documents, existing keyword retrieval methods encode each node with Dewey encoding, comparing Dewey encodings part by part is necessary in LCA computation. When the depth of XML is large, lots of LCA computations will affect the performance of keyword search. In this paper we propose a novel labeling method called Level-TRaverse (LTR) encoding, combine with the definition of the result set based on Exclusive Lowest Common Ancestor (ELCA),design a query Bottom-Up Level Algorithm(BULA).The experiments demonstrate this method improves the efficiency and the veracity of XML keyword retrieval.

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32

Ma, Zongmin, Xiaoqing Lin, Li Yan, and Zhen Zhao. "RDF Keyword Search by Query Computation." Journal of Database Management 29, no. 4 (October 2018): 1–27. http://dx.doi.org/10.4018/jdm.2018100101.

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Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the RDF data graph for composing SPARQL queries. To avoid potentially wasteful summary graph expansion, we develop a new search prioritization scheme by combining the degree of a vertex with the distance from the original keyword element. Starting from the ordered priority list that is built in advance, we apply the forward path index to faster find the top-k subgraphs, which are relevant to the conjunction of the entering keywords. The experimental results show that our approach is efficient and scalable.

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33

Zhang, Yu, Lei You, and Yin Li. "Tree-Based Public Key Encryption with Conjunctive Keyword Search." Security and Communication Networks 2021 (November 5, 2021): 1–16. http://dx.doi.org/10.1155/2021/7034944.

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Searchable public key encryption supporting conjunctive keyword search is an important technique in today’s cloud environment. Nowadays, previous schemes usually take advantage of forward index structure, which leads to a linear search complexity. In order to obtain better search efficiency, in this paper, we utilize a tree index structure instead of forward index to realize such schemes. To achieve the goal, we first give a set of keyword conversion methods that can convert the index and query keywords into a group of vectors and then present a novel algorithm for building index tree based on these vectors. Finally, by combining an efficient predicate encryption scheme to encrypt the index tree, a tree-based public key encryption with conjunctive keyword search scheme is proposed. The proposed scheme is proven to be secure against chosen plaintext attacks and achieves a sublinear search complexity. Moreover, both theoretical analysis and experimental result show that the proposed scheme is efficient and feasible for practical applications.

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34

Dosso, Dennis, and Gianmaria Silvello. "Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System." IEEE Access 8 (2020): 14089–111. http://dx.doi.org/10.1109/access.2020.2966823.

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35

V, Naga Bhargavi, and Sashi Rekha K. "Comparative Analysis of Threshold Multi-Keyword Search with Searchable Encryption of Reduced Computation Keyword Search for Cloud-Based Group Data Sharing." ECS Transactions 107, no. 1 (April 24, 2022): 16901–11. http://dx.doi.org/10.1149/10701.16901ecst.

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To reduce the computation cost of multi-keyword search for cloud-based group data sharing. Materials and methods: Multi-Keyword Advanced Encryption Standard algorithm (MAES) and Searchable Encryption RSA algorithm (SERSA) was iterated 10 times (sample size =20) for calculating the computational cost of multi-keyword search. The Shamir’s Secret key technique used with MAES algorithm helps to reduce the computation cost of multi-keyword search. Results and discussion: MAES appears to have better reduced cost (1.86 sec) compared to SERSA algorithm (2.05 sec). There was a statistical significance difference between MAES and SERSA with the value p=0.026 (p <0.05). Conclusion: MAES algorithm with Shamir's Secret key provides better reduced computation cost of multi-keyword search compared to SERSA.

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36

Liang, Zhang, Zou Fu-tai, and Ma Fan-yuan. "KRBKSS: a keyword relationship based keyword-set search system for peer-to-peer networks." Journal of Zhejiang University-SCIENCE A 6, no. 6 (June 2005): 577–82. http://dx.doi.org/10.1631/jzus.2005.a0577.

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37

Rebai, Ilyes, Yassine Ben Ayed, and Walid Mahdi. "Spoken keyword search system using improved ASR engine and novel template-based keyword scoring." Multimedia Tools and Applications 78, no. 2 (June 25, 2018): 1495–510. http://dx.doi.org/10.1007/s11042-018-6276-y.

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38

Selvaganesan, S., Su-Cheng Haw, and Lay-Ki Soon. "XDMA: A Dual Indexing and Mutual Summation Based Keyword Search Algorithm for XML Databases." International Journal of Software Engineering and Knowledge Engineering 24, no. 04 (May 2014): 591–615. http://dx.doi.org/10.1142/s0218194014500223.

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Achieving the effectiveness in relation to the relevance of query result is the most crucial part of XML keyword search. Developing an XML Keyword search approach which addresses the user search intention, keyword ambiguity problems and query/search result grading (ranking) problem is still challenging. In this paper, we propose a novel approach called XDMA for keyword search in XML databases that builds two indices to resolve these problems. Then, a keyword search technique based on two-level matching between two indices is presented. Further, by utilizing the logarithmic and probability functions, a terminology that defines the Mutual Score to find the desired T-typed node is put forward. We also introduce the similarity measure to retrieve the exact data through the selected T-typed node. In addition, grading for the query results having comparable relevance scores is employed. Finally, we demonstrate the effectiveness of our proposed approach, XDMA with a comprehensive experimental evaluation using the datasets of DBLP, WSU and eBay.

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39

Senthil Kumar, G., T. S. Shiny angel, K. Vijayakumar, Akhilesh Chamoli, Sarang Kejriwal, and N. Snehalatha. "Composite Keyword Based Search Over Data on Remote Information." IOP Conference Series: Materials Science and Engineering 1130, no. 1 (April 1, 2021): 012071. http://dx.doi.org/10.1088/1757-899x/1130/1/012071.

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40

Abdallah, Hanya M., Ahmed Taha, and Mazen M. Selim. "Cloud-Based Fuzzy Keyword Search Scheme Over Encrypted Documents." International Journal of Sociotechnology and Knowledge Development 13, no. 4 (October 2021): 82–100. http://dx.doi.org/10.4018/ijskd.2021100106.

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With the rapid growth and adoption of cloud computing, more sensitive information is centralized onto the cloud every day. For protecting this sensitive information, it must be encrypted before being outsourced. Current search schemes allow the user to query encrypted data using keywords, but these schemes do not guarantee the privacy of queries (i.e., when the user hits query more than once with the same keywords, the server can capture information about the data). This paper focuses on the secure storage and retrieval of ciphered data with preserving query privacy. The proposed scheme deploys the sparse vector space model to represent each query, which focuses on reducing the storage and representation overheads. And the proposed scheme adds a random number to each query vector. Hence, the cloud server cannot distinguish between queries with the same keywords, which ensures the privacy of the query. Experimental results show that the proposed scheme outperforms other relevant state-of-the-art schemes.

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41

YAN, Hua-yun, and Ji-hong GUAN. "Multi-keyword search over P2P based on Bloom filter." Journal of Computer Applications 30, no. 9 (November 30, 2010): 2335–38. http://dx.doi.org/10.3724/sp.j.1087.2010.02335.

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42

Hussan, Bzar Khidir. "Comparative Study of Semantic and Keyword Based Search Engines." Advances in Science, Technology and Engineering Systems Journal 5, no. 1 (January 2020): 106–11. http://dx.doi.org/10.25046/aj050114.

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43

Zhang, Xiaojun, Chunxiang Xu, Liming Mu, and Jie Zhao. "Identity-based encryption with keyword search from lattice assumption." China Communications 15, no. 4 (April 2018): 164–78. http://dx.doi.org/10.1109/cc.2018.8357694.

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44

Han, Jinguang, Ye Yang, Joseph K. Liu, Jiguo Li, Kaitai Liang, and Jian Shen. "Expressive attribute-based keyword search with constant-size ciphertext." Soft Computing 22, no. 15 (July 10, 2017): 5163–77. http://dx.doi.org/10.1007/s00500-017-2701-9.

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45

Handa, Rohit, C. Rama Krishna, and Naveen Aggarwal. "Keyword Binning-Based Efficient Search on Encrypted Cloud Data." Arabian Journal for Science and Engineering 44, no. 4 (October 12, 2018): 3559–84. http://dx.doi.org/10.1007/s13369-018-3580-9.

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46

Yuh-Jzer Joung, Li-Wei Yang, and Chien-Tse Fang. "Keyword search in DHT-based peer-to-peer networks." IEEE Journal on Selected Areas in Communications 25, no. 1 (January 2007): 46–61. http://dx.doi.org/10.1109/jsac.2007.070106.

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47

Wang, Haijiang, Xiaolei Dong, Zhenfu Cao, and Dongmei Li. "Secure and Efficient Attribute-Based Encryption with Keyword Search." Computer Journal 61, no. 8 (April 18, 2018): 1133–42. http://dx.doi.org/10.1093/comjnl/bxy031.

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48

Kim, Do-Guk, and Bonggyun Ko. "Investment Universe Construction Based on the Theme Keyword Search." IEEE Access 7 (2019): 133707–15. http://dx.doi.org/10.1109/access.2019.2939414.

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49

Jiang, Peng, Fuchun Guo, Kaitai Liang, Jianchang Lai, and Qiaoyan Wen. "Searchain: Blockchain-based private keyword search in decentralized storage." Future Generation Computer Systems 107 (June 2020): 781–92. http://dx.doi.org/10.1016/j.future.2017.08.036.

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50

Fu, Zhangjie, Yangen Liu, Xingming Sun, Yuling Liu, and Zuwei Tian. "Confusing-Keyword Based Secure Search over Encrypted Cloud Data." Mobile Networks and Applications 25, no. 1 (January 2, 2019): 125–32. http://dx.doi.org/10.1007/s11036-018-1195-8.

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