Boolean Keyword Search Research Articles

Blockchain for Achieving Accountable Outsourcing Computations in Edge Computing

Since the emergence of cloud computing, data owners are being driven to move their intricate data management systems from on-site locations to private cloud providers in order to take advantage of increased flexibility and cost savings. However, sensitive data must be encrypted before being outsourced in order to safeguard data privacy. This renders outdated the conventional method of using data, which relies on plaintext keyword searches. It is therefore crucial to activate an encrypted cloud data search service. In order to meet the effective data retrieval, need, search services must support multi-keyword queries and offer result similarity rating, given the volume of data users and documents stored in cloud storage. Similar works on searchable encryption seldom distinguish between search results and concentrate on single keyword or Boolean keyword search. I define and solve the difficult problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE) for the first time in this work. I also outline a tight set of privacy requirements that must be met in order for such a safe cloud data utilization system to be implemented.

Efficient and Privacy-Preserving Spatial Keyword Similarity Query Over Encrypted Data

As a popular and practical query type in location-based services, the spatial keyword query has been extensively studied in both academia and industry. Meanwhile, with the growing demand for data privacy, many privacy-preserving spatial keyword query schemes have been proposed to deal with queries over encrypted data. However, none of the existing schemes preserve access pattern privacy, and the recent research illustrates that leaking such privacy may incur inference attacks and thus disclose sensitive information. In addition, most existing schemes only consider the boolean keyword search, which is not quite practical and flexible in real-world applications. To address the above issues, in this paper, we propose two privacy-preserving spatial keyword similarity query schemes that can preserve full and partial access pattern privacy, respectively. First, we present a basic privacy-preserving spatial keyword similarity query scheme (PPSKS) by integrating a secure set membership test (SSMT) technique with secure circuits. After that, to improve performance, we propose a tree-based scheme (PPSKS+) by employing a new index called FR-tree together with a predicate encryption technique that can encrypt FR-tree. Formal security analysis shows that: i) our proposed schemes can protect outsourced data, query requests, and query results; ii) our PPSKS scheme can hide full access patterns, while the PPSKS+ scheme preserves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -access pattern privacy. Extensive experiments are also conducted, and the results indicate that our tree-based PPSKS+ scheme is much more efficient, almost two orders of magnitude better than our linear search PPSKS scheme in performing queries.

Assessing the multiple benefits of partially protected marine protected areas in Australia: A systematic review protocol.

There is global pressure to protect more of the world's oceans, primarily to protect biodiversity, and to fulfill the "30 by 30" goal set by the International Union for the Conservation of Nature (IUCN) that has recently been ratified under the Kunming-Montreal Global Biodiversity Framework at the fifteenth Conference of Parties (COP-15). Fully protected marine protected areas (MPAs) provide the highest level of protection for biodiversity from destructive or extractive practices and may limit access to the area itself. Fully protected MPAs (also commonly referred to as 'no-take MPAs') ban all fishing activities, thereby removing the realisation of direct economic and social benefits from resource extraction within these areas. However, fully protected MPAs can still act as source of productivity to surrounding areas, while also providing an important scientific reference role for off-reserve management thereby providing indirect economic and social outcomes, as well as biodiversity benefits. Sustainable marine resource management strives to achieve 'triple-bottom line' benefits, where economic, social, and biodiversity benefits are maximised in managed areas of the ocean. Implementing 'partially protected' areas (PPAs) in areas of high biodiversity value (i.e., inshore, productive areas of the ocean) that allow for some extractive activities, may allow us to supplement fully MPAs to meet IUCN conservation goals, while maximising social and economic benefits. However, our current understanding lacks explicit quantitative assessments of whether and how PPAs can benefit (or otherwise) biodiversity, while also providing economic and social benefits. This study provides a method to systematically review the scientific and legislative literature to understand how PPAs may contribute to conserving biodiversity while also providing social and economic benefits to Australia. The implementation of partially protected areas (PPAs) requires careful consideration of many potentially competing factors, and an understanding of the types of partial protection already in place in a region. We have developed a systematic literature review protocol focussing on the primary research question: "What is the current state of partially protected area (PPA) implementation across Australian marine areas?". The aim of the review is to provide marine resource managers with a comprehensive overview of PPAs in Australia, including associated goals and stated management strategies to achieve these goals, and a methodological approach that may be utilised globally. The review protocol was designed by the research team for a Fisheries Resource and Development Corporation (FRDC) strategic research grant and will seek input from a project steering committee for the project on aggregation of the initial results. The steering committee is made up of stakeholders from a wide range of backgrounds and interests, covering marine conservation, fisheries management, Indigenous values, and academic research in Australia. Multiple academic databases, alongside Australian Federal, State, and Territory legislation and related policies will be reviewed using Boolean keyword search strings for both academic databases and relevant grey literature. Results from eligible documents will be compiled and insights from the review collated to provide information on the status of PPA implementation in Australia.

Attribute-Based Expressive and Ranked Keyword Search Over Encrypted Documents in Cloud Computing

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.

Online/Offline Attribute-Based Boolean Keyword Search For Internet Of Things

Abstract With the rapid development of Internet of Things (IoT) and cloud computing, a large amount of IoT data has been stored and shared in cloud servers. However, sensitive IoT data may be leaked by untrusted cloud servers, which is a key problem hindering the development of IoT-cloud systems. To solve this problem, searchable encryption has been presented, which enables IoT devices to encrypt the collected data before uploading them to the cloud and make retrievals over the encrypted data by keyword queries. In this paper, we will study attribute-based keyword search (ABKS), which relieves us from cumbersome key management and provides a fine-grained access control. However, since the existing ABKS schemes are all constructed by bilinear pairings, which incurs high computational costs, they are not suitable for resource-constrained IoT devices. In this article, we propose an online/offline attribute-based boolean keyword search scheme, which can significantly reduce the online computing costs of IoT devices. In addition, our scheme can support boolean keyword search for data users, which has more flexible keyword expressivity than the existing online/offline ABKS schemes. We simulate our scheme and the experimental results show that it is both efficient and practical for the real IoT-cloud systems.

Compound Keyword Level Search to conserve Privacy in access of Encrypted Cloud

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE).We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multikeyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure.

Attribute Set-Based Boolean Keyword Search over Encrypted Personal Health Records

A personal health record (PHR) is an electronic application which enables patients to collect and share their health information. With the development of cloud computing, many PHR services have been outsourced to cloud servers. Cloud computing makes it easier for patients to manage their personal health records and makes it easier for doctors and researchers to share and access this information. However, due to the high sensitivity of PHR, a series of security protections are needed to protect them, such as encryption and access control. In this article, we propose an attribute set-based Boolean keyword search scheme, which can realize fine-grained access control and Boolean keyword search over encrypted PHR. Compared with the existing attribute-based searchable encryption, our solution can not only improve the flexibility in specifying access policies but also perform Boolean keyword search, which can meet the needs of large-scale PHR users. Furthermore, we simulate our scheme, and the experimental results show that our scheme is practical for PHR systems in cloud computing.

Behavioural economics in fisheries: A systematic review protocol.

BackgroundThe field of behavioural economics holds several opportunities for integrated fisheries management and conservation and can help researchers and managers alike understand fisher behaviour and decision-making. As the study of the cognitive biases that influence decision-making processes, behavioural economics differentiates itself from the classical field of economics in that it does not assume strictly rational behaviour of its agents, but rather looks for all mechanisms that influence behaviour. This field offers potential applications for fisheries management, for example in relation to behavioural change, but such applications require evidence of these mechanisms applied in a fisheries context. Thus, we have developed a systematic literature review protocol focusing on the primary question: “Which behavioural economics mechanisms influence fisher behaviour?” The aim is to provide a comprehensive overview of these different mechanisms and how they have been applied in the study of fisher behaviour.Methods and expected outputsThe review protocol was developed in close collaboration with the International Council for the Exploration of the Sea (ICES) Working Group on Maritime Systems (WGMARS). WGMARS members were therefore considered the key stakeholders for this study, and were consulted to develop a suitable systematic review question and methodology. Three academic databases will be searched using a customized Boolean keyword search string. Research articles deemed eligible for inclusion in the systematic review are those that studied the influence of behavioural-economics mechanisms on the behaviour of marine fishers in any location, and at any scale. Insights from this literature will be collated in order to provide an overview of the relevant behavioural-economics mechanisms and actions, how effective these mechanisms are and at what scale, geographic region and in which fisheries sector they have been applied. Any fisheries management implications identified by the studies under review will also be outlined. Finally, it will be recorded whether or not ethical considerations were made in the reviewed literature, so that in the discussion it will be possible to reflect on the ethics of conducting behavioural-economics research and policy actions in a fisheries context.

Attribute-Based Hybrid Boolean Keyword Search over Outsourced Encrypted Data

With cloud computing becoming increasingly popular, there has been a rapid increase in the number of data owners who outsource their data to the cloud while allowing users to retrieve the data. To preserve the privacy of data, data owners usually encrypt their data before outsourcing them to the cloud, and cloud servers can search across the ciphertext domain on behalf of users without learning any information about the data. However, existing work in the literature mostly supports only a single-user or single-keyword search which is not able to satisfy more desired expressive search. Thus, we propose a searchable encryption primitive with attribute-based access control for hybrid boolean keyword search over outsourced encrypted data. There exist several desirable features: (1) Data owners can set search permissions for outsourced encrypted data according to an access control policy. (2) Multiple users, whose attributes satisfy the access control policy, are allowed to perform a retrieval operation upon the encrypted data. (3) Authorized users are able to perform more expressive search, such as any required boolean keyword expression search. Additionally, this primitive is provably secure under our security model and we have also implemented the prototype to show the practicality of the primitive.

Multi-user Boolean searchable encryption supporting fast ranking in mobile clouds

Mobile cloud computing has become an important technology for mobile services. It overcomes physical limitations of mobile devices towards flexible and scalable mobile services. When using mobile clouds, people usually upload and store their data in cloud servers, and access the data through mobile devices from anywhere. As the privacy of the sensitive data is the major concern of users, the data is often encrypted before storing in cloud servers. However, the flexibility using the data is thereby affected, such as a Boolean search over the encrypted data, or ranking search results. To address these problems, in this paper we first propose a novel multi-user Boolean keyword search scheme (MBKSS) to achieve a rapid Boolean query outcome, while eliminating query interactions between users and the data owner. To rank search results and protect the privacy of relevance scores between files and keywords, we design a new homomorphic cryptosystem with partial decryption, which could be used as a basis for constructing a fast ranking search protocol (FRSP). A comprehensive security analysis shows that the proposed MBKSS and FRSP can achieve secure Boolean search and ranking. The experimental results demonstrate that the proposed MBKSS and FRSP are efficient and suitable for mobile devices.

Multi-writer Multi-reader Boolean Keyword Searchable Encryption

In traditional public key searchable encryption (PKSE), a data owner (writer) utilizes data user’s (reader) public key to build ciphertexts. Thus, to share D data items (W keywords per item) with R readers, a writer suffers from $$O(R \cdot D \cdot W)$$ computational overhead. Researchers then offer numerous schemes supporting multiple readers with optimal overhead, i.e., $$O(D \cdot W)$$ . However, these schemes support either a single-keyword search or multi-keyword search in single-writer multi-reader (SWMR) setting where a writer could share data with the known set of readers. On the other hand, the existing multi-writer multi-reader (MWMR) PKSE offers Boolean search but suffers from $$O(R \cdot D \cdot W)$$ computational overhead for each writer. We observe applications desiring PKSE where writers could share data with the unknown set of readers and readers could perform search across data for Boolean query. Since the existing literature lacks such schemes, we propose a multi-writer multi-reader Boolean keyword searchable encryption (MWMR-BKSE) where the separate sets of registered readers and writers are prepared. Once registered, the writer could compute ciphertexts without knowing the potential readers and the readers could search across data at any time. The computational overhead on each writer is optimal $$O(D \cdot W)$$ . Additionally, a deregistered client would not be able to further write or read data. With MWMR-BKSE, we offer a Boolean keyword search utilizing key policy attribute-based encryption. The security of ciphertext against chosen keyword attack is proved using full security model. With the detailed theoretical analysis and extensive simulations on real-world datasets, we demonstrate the effectiveness of MWMR-BKSE.

An Efficient and Secure Multilevel Keyword Ranked Search for Encrypted Data

with the growing digital communication and networks, the data owners are motivated to outsource their complex data to the global storage space. Greater flexibility and economic saving are the advantages of this global storage space. Before outsourcing the sensitive data, it has to be encrypted in order to enforce the data privacy. In the encrypted data, search service is important to get the necessary data. The stored data is relatively large so it requires multiple keywords in the search query and it will return document in the order of their relevance to these keywords searched. Related works on searchable encryption focus on single keyword search or Boolean keyword search and rarely sort the result and for the multi-keyword search coordinate matching, i.e., as many matches as possible, to effectively capture the relevance of outsourced documents to the query keywords and inner product similarity to evaluate such similarity measure. In Multi-keyword Ranked Search under the coordinate matching, the ranking helps for the efficient retrieval. The multilevel keyword ranked search is implemented by using the cache to reduce the search time.

Privacy Preserving Ranked Multi-Keyword Search for Multiple Data Owners in SaaS Cloud Computing

The innovation in cloud computing has encouraged the data owners to outsource their data managing system from local sites to profitable public cloud to increase affordability of services. But people can like full benefit of cloud computing, if we are able to report very real secrecy and security concerns that come with loading sensitive personal information then we have to increase security, privacy. In view of the huge number of data users, documents in the cloud, it is important for the search facility to agree multi keywords query and arrange for result comparison ranking to meet the actual need of data recovery search and not regularly distinguish the search results. Related mechanisms on searchable encryption emphasis on single keyword search or Boolean keyword search, and often sort the search outcomes. In our system, we explain and solve the interesting problem of privacy preserving multi keywords ranked search over encrypted cloud data, and create a set of strict privacy necessities for such a safe cloud data application system to be effected in real. We can add Document owner accounts, Cloud Server account, Admin account and grant permission to various privileges to different user.

Publicly verifiable searchable symmetric encryption based on efficient cryptographic components

Public verifiability is an interesting feature that cryptographic protocols, such as those used in cloud computing applications, may support. By public verifiability, the client can delegate the verification process to a third party auditor without revealing the private key or data. The main contribution of this paper is achieving public verifiability in the symmetric setting of searchable encryption (SE), separately for single and Boolean keyword search. While Public verifiability in SE has already been achieved using complex tools such as indistinguishability obfuscation or pairing, this work employs basic cryptographic components and assumptions, such as pseudo-random functions, one-way functions, digital signatures and the DDH assumption.

Systematic Review of Hearing Preservation After Radiotherapy for Vestibular Schwannoma.

To determine the long-term hearing preservation rate for spontaneous vestibular schwannoma treated by primary radiotherapy. The MEDLINE/PubMed, Web of Science, Cochrane Reviews, and EMBASE databases were searched using a comprehensive Boolean keyword search developed in conjunction with a scientific librarian. English language papers published from 2000 to 2016 were evaluated. Inclusion criteria: full articles, pretreatment and posttreatment audiograms or audiogram based scoring system, vestibular schwannoma only tumor type, reported time to follow-up, published after 1999, use of either Gamma Knife or linear accelerator radiotherapy. case report or series with fewer than five cases, inadequate audiometric data, inadequate time to follow-up, neurofibromatosis type 2 exceeding 10% of study population, previous treatment exceeding 10% of study population, repeat datasets, use of proton beam therapy, and non-English language. Two reviewers independently analyzed papers for inclusion. Class A/B, 1/2 hearing was defined as either pure tone average less than or equal to 50 db with speech discrimination score more than or equal to 50%, American Academy of Otolaryngology-Head & Neck Surgery (AAO-HNS) Hearing Class A or B, or Gardner-Robertson Grade I or II. Aggregate data were used when individual data were not specified. Means were compared with student t test. Forty seven articles containing a total of 2,195 patients with preserved Class A/B, 1/2 hearing were identified for analysis. The aggregate crude hearing preservation rate was 58% at an average reporting time of 46.6 months after radiotherapy treatment. Analysis of time-based reporting shows a clear trend of decreased hearing preservation extending to 10-year follow-up. This data encourages a future long-term controlled trial.

Dynamic and Secure Multi Keyword Ranked Search Scheme on encrypted data

The major aim of this paper is to solve the problem of multi-keyword ranked search over encrypted cloud data (MRSE) at the time of protecting exact method wise privacy in the cloud computing concept. Data holders are encouraged to outsource their difficult data management systems from local sites to the business public cloud for large flexibility and financial savings. However for protecting data privacy, sensitive data have to be encrypted before outsourcing, which performs traditional data utilization based on plaintext keyword search. As a result, allowing an encrypted cloud data search service is of supreme significance. In view of the large number of data users and documents in the cloud, it is essential to permit several keywords in the search demand and return documents in the order of their appropriate to these keywords. Similar mechanism on searchable encryption makes centre on single keyword search or Boolean keyword search, and rarely sort the search results. In the middle of various multi-keyword semantics, deciding the well-organized similarity measure of “coordinate matching,” it means that as many matches as possible, to capture the appropriate data documents to the search query. Particularly, we consider “inner product similarity” i.e., the amount of query keywords shows in a document, to quantitatively estimate such match measure that document to the search query. Through the index construction, every document is connected with a binary vector as a sub index where each bit characterize whether matching keyword is contained in the document. The search query is also illustrates as a binary vector where each bit means whether corresponding keyword appears in this search request, so the matched one could be exactly measured by the inner product of the query vector with the data vector. On the other hand, directly outsourcing the data vector or the query vector will break the index privacy or the search privacy. The vector space model facilitate to offer enough search accuracy, and the DES encryption allow users to occupy in the ranking while the popularity of computing work is done on the server side by process only on cipher text. As a consequence, data leakage can be eradicated and data security is guaranteed.

Fuzzy Based Implementation of Multi - Keyword Ranked Search over Encrypted Cloud Data in Secure Cloud Environment

Cloud computing enables the paradigm of data outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service is a challenging task. Even though searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. Hence it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to the keywords. The works on searchable encryption focused on single keyword search or Boolean keyword search and the result produced by them are rarely sorted. An effective method proposed for this challenging problem is privacy-preserving search over encrypted cloud data. This method establishes a set of strict privacy requirements for such a secure cloud data utilization system through MRSE. Among various multi-keyword semantics, this method chooses the efficient similarity measure of “coordinate matching”. Then according to Top K Query method the sorted results are produced. The privacy is preserved by the chunk of data stored in a various server’s. Then further improvisation is taken to introduce low overhead on computation and communication in future.

REVIEW ON SECURE AND DYNAMIC MULTI-KEYWORD RANKED SEARCH SCHEME OVER ENCRYPTED CLOUD DATA

Present days growing popularity of cloud computing, the data owners are outsource their data to cloud servers for prominent convenience and reduced cost in facts control. However, sensitive information need to be encrypted before outsourcing for privacy requirements, which obsoletes statistics usage like key-word-primarily based record retrieval. In this paper we resolve the trouble of multi-keyword ranked search over encrypted cloud data (MRSE) which simultaneously supports dynamic update operations like deletion and insertion of files sensible information inside the cloud computing concept. As an end result, allowing an encrypted cloud. In view of the huge variety of information customers and files within the cloud, it's far vital to allow several key phrases inside the seek call for and go back files inside the order in their applicable to these key phrases. comparable mechanism on searchable encryption makes centre on unmarried key-word seek or Boolean keyword search, and infrequently type the quest effects. within the middle of numerous multi-key-word semantics, determining the properly-prepared similarity measure of “coordinate matching,” it way that as many fits as feasible, to seize the suitable data documents to the hunt query. in particular, we recall “internal product similarity” and additionally we construct tree-based totally index shape, every file is hooked up with a binary vector as a sub index where each bit symbolize whether matching keyword is contained within the file. The Ranked result presents pinnacle ok retrieval results. Additionally we advise an alert machine which will generate alerts whilst un-legal user attempts to get right of entry to the information from cloud, the alert will generate inside the shape of mail and message. And the ElGamal Cryptosystem allow customers to occupy inside the rating whilst the recognition of computing work is completed on the server facet via method handiest on cipher textual content which leads information leakage and statistics safety is confident.

MULTI KEYWORD RANKED SEARCH OVER ENCRYPTED CLOUD DATA

with the initiation of cloud computing, data possessors are driven to contract out their Complex data management systems from local sites to the commercial community cloud for great suppleness and financial savings. But for defending data privacy, complex data have to be encrypted before outsourcing, which obsoletes traditional data use based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of supreme position. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their significance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the Search results. We describe and crack the stimulating problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE).

Confidentiality-conserving Multi-keyword Ranked Search Above Encrypted Cloud Data

In cloud computing, data owners outsource their complex data management systems from local sites to the public cloud for great flexibility and cost-effective. But before outsourcing, sensitive data have to be encrypted for protecting data privacy. This obsoletes traditional data utilization based on plaintext keyword search. Thus, allowing an encrypted cloud data search service is of vital importance. As large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. In literature of searchable encryption single keyword search or Boolean keyword search techniques are discussed. In this paper, we propose the problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). In which the queries are transferred to the server. Server searches the relevant content by using the coordinate matching and sends the results to the user. Then the user decrypts the data.