scholarly journals MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers

2018 ◽  
Vol 10 (5) ◽  
pp. 38
Author(s):  
Jingsha He ◽  
Jianan Wu ◽  
Nafei Zhu ◽  
Muhammad Salman Pathan
Keyword(s):  
Keyword Search ◽  
Encrypted Data ◽  
Fuzzy Keyword Search ◽  
Cloud Servers

scholarly journals Efficient verifiable fuzzy keyword search over encrypted data in cloud computing

2013 ◽  
Vol 10 (2) ◽  
pp. 667-684 ◽  
Author(s):  
Jianfeng Wang ◽  
Hua Ma ◽  
Qiang Tang ◽  
Jin Li ◽  
Hui Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Efficiency Analysis ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Proposed Model ◽  
Cloud Server ◽  
Encryption Schemes ◽  
User Data ◽  
Fuzzy Keyword Search

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud by users. To maintain the confidentiality of sensitive user data against untrusted servers, the data should be encrypted before they are uploaded. However, this raises a new challenge for performing search over the encrypted data efficiently. Although the existing searchable encryption schemes allow a user to search the encrypted data with confidentiality, these solutions cannot support the verifiability of searching result. We argue that a cloud server may be selfish in order to save its computation ability or bandwidth. For example, it may execute only a fraction of the search and returns part of the searching result. In this paper, we propose a new verifiable fuzzy keyword search scheme based on the symbol-tree which not only supports the fuzzy keyword search, but also enjoys the verifiability of the searching result. Through rigorous security and efficiency analysis, we show that our proposed scheme is secure under the proposed model, while correctly and efficiently realizing the verifiable fuzzy keyword search. The extensive experimental results demonstrate the efficiency of the proposed scheme.

scholarly journals Secure Multi-keyword Similarity Search Over Encrypted Data With Security Improvement

2021 ◽  
Vol 17 (2) ◽  
pp. 1-10
Author(s):  
Hussein Mohammed ◽  
Ayad Abdulsada
Keyword(s):  
Real World ◽  
Similarity Search ◽  
Keyword Search ◽  
Bloom Filter ◽  
Locality Sensitive Hashing ◽  
Real World Data ◽  
Encrypted Data ◽  
Search Results ◽  
Security Properties ◽  
Cloud Servers

Searchable encryption (SE) is an interesting tool that enables clients to outsource their encrypted data into external cloud servers with unlimited storage and computing power and gives them the ability to search their data without decryption. The current solutions of SE support single-keyword search making them impractical in real-world scenarios. In this paper, we design and implement a multi-keyword similarity search scheme over encrypted data by using locality-sensitive hashing functions and Bloom filter. The proposed scheme can recover common spelling mistakes and enjoys enhanced security properties such as hiding the access and search patterns but with costly latency. To support similarity search, we utilize an efficient bi-gram-based method for keyword transformation. Such a method improves the search results accuracy. Our scheme employs two non-colluding servers to break the correlation between search queries and search results. Experiments using real-world data illustrate that our scheme is practically efficient, secure, and retains high accuracy.

scholarly journals Enabling Efficient Verifiable Fuzzy Keyword Search Over Encrypted Data in Cloud Computing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 45725-45739 ◽  
Author(s):  
Xinrui Ge ◽  
Jia Yu ◽  
Chengyu Hu ◽  
Hanlin Zhang ◽  
Rong Hao
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Encrypted Data ◽  
Fuzzy Keyword Search

scholarly journals Verifying Fuzzy Keyword Search Over Cipher Text in Cloud Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 3252-3257
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Searchable Encryption ◽  
Knowledge User ◽  
Cloud Server ◽  
Encryption Schemes ◽  
Fuzzy Keyword Search ◽  
Word Search ◽  
To Come ◽  
Cloud Servers

To enhance the potency of knowledge looking out, most knowledge house owners store their knowledge files in numerous cloud servers within the kind of ciphertext. Thus, economical search victimization fuzzy keywords become a vital issue in such a cloud computing atmosphere. Searchable cryptography will support knowledge user to select and retrieve the cipher documents over encrypted cloud knowledge by keyword-based search. Most of the prevailing searchable encryption schemes solely specialize in the precise keyword search. When knowledge user makes writing system errors, these schemes fail to come to the results of interest. In searchable encryption, the cloud server may come to the invalid result to knowledge user for saving the computation price or alternative reasons. Therefore, these precise keyword search schemes notice very little sensible significance in real-world applications. So as to deal with these problems, we tend to propose unique verifiable fuzzy keyword search theme over encrypted cloud knowledge. We tend to propose a verifiable precise keyword search theme which extend this theme to the fuzzy keyword search theme. Here we tend to thus propose a system for fuzzy keyword sets rather than precise word search. This will help us drastically to reduce the costs and it also allows to have multi-users using the system simultaneously.

Efficient fuzzy keyword search scheme over encrypted data in cloud computing based on Bed-tree index structure

2021 ◽  
pp. 1-13
Author(s):  
Dongping Hu ◽  
Aihua Yin
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Hamming Distance ◽  
Security Analysis ◽  
Index Structure ◽  
Search Performance ◽  
Encrypted Data ◽  
Fuzzy Keyword Search ◽  
Work Security ◽  
And Performance

In cloud computing, enabling search directly over encrypted data is an important technique to effectively utilize encrypted data. Most of the existing techniques are focusing on fuzzy keyword search as it helps achieve more robust search performance by tolerating misspelling or typos of data users. Existing works always build index without classifying keywords in advance. They suffer from efficiency issue. Furthermore, Euclidean distance or Hamming distance is often chosen to evaluate strings’ similarity, ignoring prefixes matching and the influence of strings’ length on the accuracy. We propose an efficient fuzzy keyword search scheme with lower computation cost and higher accuracy to address the aforementioned problems. We employ the sub-dictionaries technique and the Bed-tree structure to build an index with three layers for achieving better search efficiency. With this index structure, the server could locate the keyword and could narrow the search scope quickly. The Jaro-Winkler distance is introduced to qualify the strings’ similarity by considering the prefixes matching and string length. The secure privacy mechanism is incorporated into the design of our work. Security analysis and performance evaluation demonstrate our scheme is more efficient compared to the existing one while guaranteeing security.

scholarly journals Implementation of Fuzzy Keyword Search over Encrypted Data in Cloud Computing

2015 ◽  
Vol 45 ◽  
pp. 499-505 ◽  
Author(s):  
Narendra Shekokar ◽  
Kunjita Sampat ◽  
Chandni Chandawalla ◽  
Jahnavi Shah
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Encrypted Data ◽  
Fuzzy Keyword Search

scholarly journals A Survey on Secure Ranked Keyword Search over Outsourced Encrypted Cloud Data

2021 ◽  
Vol 9 (VI) ◽  
pp. 949-952
Author(s):  
Akash Tidke
Keyword(s):  
Comparative Study ◽  
Keyword Search ◽  
Encrypted Data ◽  
Cloud Data ◽  
Ranked Search ◽  
Ranked Keyword Search ◽  
Cloud Servers

In this paper we present a survey on keyword based searching algorithms. Various searching techniques are used for retrieving the encrypted data from cloud servers. This survey work involves a comparative study of these keyword based searching algorithms. It concludes that till now multi-keyword ranked search MRSE scheme is the best methodology for searching the encrypted data.

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