Multi-keywords fuzzy search encryption supporting dynamic update in an intelligent edge network

Background : Instant search recommends completions of the query ‘on the fly’, and instantly displays the results with every keystroke. It is desirable that these query results be robust against typographical errors that appear not only in the query but also in the documents. Additionally, instant search requires instant response time and ranking of the results to focus on the most important answers. Method: In this study, simple and efficient methods for instant fuzzy single keyword and multi-keyword search that are resilient to typographical errors and that employ no more than inverted and forward indices are studied. While computing search results incrementally using the cached results, the answers are ranked based on their relevance to the query using probabilistic correlation-based ranking. Findings: Experiments are conducted on data sets DBLP and Medline and the execution time for obtaining answers to instant fuzzy single keyword search is recorded for different prefix lengths. Similarly, the execution time for obtaining answers to instant fuzzy multi-keyword search is recorded for sub-queries of two keywords and three keywords for various prefix lengths on the same data set. Furthermore, in order to measure the usefulness of the proposed correlation-based ranking, precision is calculated for the search results. Experimental evaluation demonstrates the efficacy of the instant fuzzy search algorithms and the probabilistic correlation-based ranking. Applications: The proposed instant fuzzy keyword search for single and multiple keywords not only improves the efficiency but also the quality of the search results. Keywords: Keyword Search, Multi-keyword Search, Fuzzy Search, Probabilistic Correlation

Searchable encryption is an efficient and secure solution that can perform search operation in ciphertext on cloud servers. It is widely used by a growing number of businesses and individuals. However, most existing searchable encryption schemes typically overestimate the security level of the cloud server, which is an untrusted and centralized third party in fact. The emergence of blockchain technology solves the trustworthiness problem of the third party with features such as decentralization, verifiability and immutability. The combination of searchable encryption and blockchain technology uses the smart contract instead of the cloud server to execute ciphertext search task, which improves security and efficiency of search significantly. We propose a Fuzzy Multi-keyword Search Scheme for Encrypted Cloud Data based on Multi-chain Network (FMSM). FMSM is a distributed searchable encryption scheme since we adopt a multi-chain architecture. It not only supports fuzzy multi-keyword search and dynamic updates for encrypted cloud data, but also supports parallel processing of data. In FMSM, counting bloom filter and MinHash algorithm are used to construct index vectors and query vectors on the corresponding two chains, besides, the search is done on a third chain. All of these calculation tasks are executed in the way of distributed computing, which makes the index construction and search process more efficient and the sorting results more accurate. The security analysis and performance evaluation show that the FMSM has high security, reliability, search efficiency and accuracy.

In cloud computing, documents can be outsourced to the cloud server to achieve flexible access control and efficient sharing among multiple users. The outsourced documents can be intelligently searched according to some keywords with the help of cloud server. During the search process, some private information of outsourced documents may be leaked since the keywords may contain sensitive information of users. However, existing privacy-preserving keyword search schemes have high computation complexity, which are not suitable for resource-constrained end devices—that is, the data processing and search trapdoor generation procedures require users to take resource-intensive computations, e.g., high-dimensional matrix operations, exponentiations and bilinear pairings, which are unaffordable by resource-constrained devices. To address the issues of efficiency and privacy for realizing sorted multi-keyword search over outsourced data in clouds, this paper proposes a lightweight privacy-preserving ranked multi-keyword search (PRMS) scheme, which is further extended to allow each outsourced document to be associated with multiple types of keywords. The searched documents can be sorted according to their similarity scores between the search query and the keyword index of documents, so that only when the similarity score exceeds a given threshold, the corresponding searched document will be returned. The security analysis demonstrates that the proposed PRMS schemes can guarantee the privacy of outsourced documents and keywords, and provides unlinkability for search trapdoors. Performance analysis and comparison show the practicality of the proposed PRMS schemes.

Public-key encryption with keyword search (PEKS) is a versatile tool. It allows a third party knowing the search trapdoor of a keyword to search encrypted documents containing that keyword without decrypting the documents or knowing the keyword. However, it is shown that the keyword will be compromised by a malicious third party under a keyword guess attack (KGA) if the keyword space is in a polynomial size. We address this problem with a keyword privacy enhanced variant of PEKS referred to as public-key encryption with fuzzy keyword search (PEFKS). In PEFKS, each keyword corresponds to an exact keyword search trapdoor and a fuzzy keyword search trapdoor. Two or more keywords share the same fuzzy keyword trapdoor. To search encrypted documents containing a specific keyword, only the fuzzy keyword search trapdoor is provided to the third party, i.e., the searcher. Thus, in PEFKS, a malicious searcher can no longer learn the exact keyword to be searched even if the keyword space is small. We propose a universal transformation which converts any anonymous identity-based encryption (IBE) scheme into a secure PEFKS scheme. Following the generic construction, we instantiate the first PEFKS scheme proven to be secure under KGA in the case that the keyword space is in a polynomial size.

Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al. , who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang’s scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang’s scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al. ’s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al. ’s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.

Efficient Multi-Keyword Search in Encrypted and Distributed Cloud Storage

More and more sensitive information of big data are being now stored in the cloud for it offers quality service and convenient management. But the Cloud Server cannot be fully trusted because it may leak information which raises security and privacy concerns of the stored data. Therefore, sensitive data has to be encrypted before shifted to the cloud. It is a great convenience for authorized users to directly retrieve the encrypted data stored in the cloud by keyword search. As we all known, returning all the matching documents is a waste of network bandwidth. Although there are some schemes that support sorting fuzzy multi-keyword search, and they are generally based on edit distance to looking for fuzzy alternatives set, which leads to significantly larger index file size and higher search complexity. Then we rank the search results according to the matching score between keywords and documents. In this paper, we give a novel multi-keyword fuzzy and sortable search scheme. We use n-gram technology to realize fuzzy search and give a novel ranking search scheme based on the fuzzy multi- keyword search. For the matching files may be more than one, there are differences between the original query keywords and the fuzzy keywords. When ranking files, we will not only consider the relevance score between the index keywords and the documents, but also the similarity between the query and keywords. Thus we calculate the comprehensive matching score of the file to the set of query keywords, which is more efficient and accurate.

In vehicular networks, caching content on an edge server (ES) is a popular method for quickly responding to massive vehicle service requests, reducing communication delays, and enhancing driver and passenger service experiences. However, after integrating ESs with vehicular networks to provide vehicles access to the cached content in these ESs, significant challenges regarding protecting the privacy of vehicle data and communication security arise. In this study, to address security and privacy-preserving issues, we propose a secure and revocable cache-based distributed data sharing scheme for vehicular networks wherein a token authentication mechanism and multi-authority ciphertext-policy attribute-based encryption are integrated. In this scheme, both authentication and authorization capabilities are delegated to an ES while restricting access to service content to only legal vehicles, achieving proper access control between vehicles and ESs, and effectively preserving the privacy of vehicle data. Moreover, we attributed the revocations of ESs to the associated attribute authorities, eliminating the need for a system-wide update of keying materials. Through rigorous security proofs and detailed security analyses, we demonstrate that the scheme meets the security requirements of vehicular networks and can resist more security attacks. The proposed scheme achieves better balance between computational and communication costs than related schemes.

Searchable encryption (SE) allows a client to store his data in the Cloud in a way that it is encrypted but still searchable by the server. However, when doing multi-keywords search (MKS), most SE schemes seems to be rather inefficient. In this paper, we design and implement an SE scheme that allows the MKS indices to be pre-built secretly at the server side, making fast MKS on very large databases possible. Detailed experiments and analysis are given, showing that our scheme is efficient, while the possible information leakages during the construction are proved to be minimized.

With the prevalence of cloud computing, a growing number of users are delegating clouds to host their sensitive data. To preserve user privacy, it is suggested that data is encrypted before outsourcing. However, data encryption makes keyword-based searches over ciphertexts extremely difficult. This is even challenging for <i>fuzzy search</i> that allows uncertainties or misspellings of keywords in a query. In this article, we propose a prime inner product encoding (PIPE) scheme, which makes use of the <i>indecomposable</i> property of prime numbers to provide efficient, highly accurate, and flexible multi-keyword fuzzy search. Our main idea is to encode either a query keyword or an index keyword into a vector filled with primes or reciprocals of primes, such that the result of vectors’ inner product is an integer only when two keywords are similar. Specifically, we first construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math></inline-formula> that is secure in the known ciphertext model. Unlike existing works that have difficulty supporting AND and OR semantics simultaneously, <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math></inline-formula> gives users the flexibility to specify different search semantics in their queries. Then, we construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{\text{S}}$</tex-math></inline-formula> that subtly adds random noises to a query vector to resist linear analyses. Both theoretical analyses and experiment results demonstrate the effectiveness of our scheme.

Efficient Multi Keyword Search In Heterogeneous Environment Based On Ranking Technique

Recently, Li et al. introduced a fuzzy keyword search over encrypted data in Cloud Computing. Their approach relies on fuzzy keyword sets which are used by a symmetric searchable encryption protocol. The idea behind these fuzzy keyword sets is to index – before the search phase – the exact keywords but also the ones differing slightly according to a fixed bound on the tolerated edit distance. We here suggest a different construction. We exploit a classical embedding of the edit distance into the Hamming distance. This enables us to adapt results on private identification schemes to this new context. This way of doing implies more flexibility on the tolerated edit distance.

With the development of cloud computing, the sensitive information of outsourced data is at risk of unauthorized accesses. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. Hence, it is an especially important thing to explore secure encrypted cloud data search service. Considering the huge number of outsourced data, there are three problems we are focused on to enable efficient search service: multi-keyword search, result relevance ranking and dynamic update. In this paper, we propose a practically efficient and flexible searchable encrypted scheme which supports both multi-keyword ranked search and dynamic update. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search result. To improve search efficiency, we design a tree-based index structure which supports insertion and deletion update well without privacy leakage. We propose a secure search scheme to meet the privacy requirements in the threat model. Finally, experiments on real-world dataset are implemented to demonstrate the overall performance of the proposed scheme, which show our scheme is efficient.

Smart home applications make our lives more comfortable, more convenient than ever before. However, deploying smart home applications and smart terminals could pose a potential security threat to personal information and home privacy. In order to prevent illegal use of terminals and applications, it is very necessary to establish secure and reliable communication between terminal and edge server. In this paper, we design a two-party authentication and key negotiation protocol for the smart terminal and edge service. The edge-based authentication and key negotiation scheme offloads the terminal’s main computational overhead to the edge side, and exploits cryptographic algorithms to achieve user anonymity and untraceability. Security is verified by the BAN logic and AVISPA. We also evaluate the performance by comparing our scheme with other related schemes in terms of computational overhead. The security and performance results show that our proposed scheme is suitable for edge-assisted smart home applications.KeywordsSmart homeAuthentication session key negotiationElliptic curve codeEdge service

Mobile Edge Computing (MEC) is a promising computing paradigm that provides cloud computing services in proximity to end users. Due to the bursty and spatially imbalanced arrival of computation tasks, the workload on different edge servers may vary wildly. To improve the Quality of Experience (QoE), peer offloading has been proposed as an effective cooperation method that offloads tasks from busy edge servers to idle ones. Although the average latency has been extensively considered in the design of peer offloading strategies, the worst-case latency, a common Quality of Service (QoS) requirement that is usually demanded by latency-sensitive applications, yet receives much less attention. In this paper, we study the task scheduling among collaborative edge servers and propose an online algorithm that aims to maximize the system utility under the worst-case latency requirement and long-term energy consumption constraints. Both theoretical analysis and simulation results demonstrate that our algorithm performs well under various situations.