Survey Paper on Multi Keyword Similarity Search over Encrypted Cloud Data

The tremendous amount of data outsourced every day by individuals or each enterprises . It is impossible to manage or to store this complex data at individual level, as the chances of crash the system is more, and the system becomes the single point of failure.When we feel the need of storing the data in such a way that it can be accessed uninterruptedly, then there the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive. Considering the privacy of the data over the cloud, for that searchable encryption can be used. At the time of retrieval of data, consider the multi-keyword search over outsourced cloud text data only as it can handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Searchable encryption also enables searching without revealing any additional information. Using multi-keyword similarity search cloud returns the files containing more number of matches with user input keywords and similar keyworks. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Filter’s bit pattern is used to speedup and it is efficient in terms of the search time at the cloud side. This paper presents a review on various existing Similarity searching techniques

Novel Proposed Work for Empirical Word Searching in Cloud Environment

People's lives have become much more convenient as a result of the development of cloud storage. The third-party server has received a lot of data from many people and businesses for storage. Therefore, it is necessary to ensure that the user's data is protected from prying eyes. In the cloud environment, searchable encryption technology is used to protect user information when retrieving data. The versatility of the scheme is, however, constrained by the fact that the majority of them only offer single-keyword searches and do not permit file changes.A novel empirical multi-keyword search in the cloud environment technique is offered as a solution to these issues. Additionally, it prevents the involvement of a third party in the transaction between data holder and user and guarantees integrity. Our system achieves authenticity at the data storage stage by numbering the files, verifying that the user receives a complete ciphertext. Our technique outperforms previous analogous schemes in terms of security and performance and is resistant to inside keyword guessing attacks.The server cannot detect if the same set of keywords is being looked for by several queries because our system generates randomized search queries. Both the number of keywords in a search query and the number of keywords in an encrypted document can be hidden. Our searchable encryption method is effective and protected from the adaptive chosen keywords threat at the same time

OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

© 2017 Pervez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search (OS2) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, OS2 ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables OS2 to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of OS2 is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations

Towards a secure and efficient search over encrypted cloud data

Includes bibliographical references.2016 Summer.Cloud computing enables new types of services where the computational and network resources are available online through the Internet. One of the most popular services of cloud computing is data outsourcing. For reasons of cost and convenience, public as well as private organizations can now outsource their large amounts of data to the cloud and enjoy the benefits of remote storage and management. At the same time, confidentiality of remotely stored data on untrusted cloud server is a big concern. In order to reduce these concerns, sensitive data, such as, personal health records, emails, income tax and financial reports, are usually outsourced in encrypted form using well-known cryptographic techniques. Although encrypted data storage protects remote data from unauthorized access, it complicates some basic, yet essential data utilization services such as plaintext keyword search. A simple solution of downloading the data, decrypting and searching locally is clearly inefficient since storing data in the cloud is meaningless unless it can be easily searched and utilized. Thus, cloud services should enable efficient search on encrypted data to provide the benefits of a first-class cloud computing environment. This dissertation is concerned with developing novel searchable encryption techniques that allow the cloud server to perform multi-keyword ranked search as well as substring search incorporating position information. We present results that we have accomplished in this area, including a comprehensive evaluation of existing solutions and searchable encryption schemes for ranked search and substring position search

Secure Remote Storage of Logs with Search Capabilities

Dissertação de Mestrado em Engenharia InformáticaAlong side with the use of cloud-based services, infrastructure and storage, the use of application logs in business critical applications is a standard practice nowadays. Such application logs must be stored in an accessible manner in order to used whenever needed. The debugging of these applications is a common situation where such access is required. Frequently, part of the information contained in logs records is sensitive. This work proposes a new approach of storing critical logs in a cloud-based storage recurring to searchable encryption, inverted indexing and hash chaining techniques to achieve, in a unified way, the needed privacy, integrity and authenticity while maintaining server side searching capabilities by the logs owner. The designed search algorithm enables conjunctive keywords queries plus a fine-grained search supported by field searching and nested queries, which are essential in the referred use case. To the best of our knowledge, the proposed solution is also the first to introduce a query language that enables complex conjunctive keywords and a fine-grained search backed by field searching and sub queries.A gerac¸ ˜ao de logs em aplicac¸ ˜oes e a sua posterior consulta s˜ao fulcrais para o funcionamento de qualquer neg´ocio ou empresa. Estes logs podem ser usados para eventuais ac¸ ˜oes de auditoria, uma vez que estabelecem uma baseline das operac¸ ˜oes realizadas. Servem igualmente o prop´ osito de identificar erros, facilitar ac¸ ˜oes de debugging e diagnosticar bottlennecks de performance. Tipicamente, a maioria da informac¸ ˜ao contida nesses logs ´e considerada sens´ıvel. Quando estes logs s˜ao armazenados in-house, as considerac¸ ˜oes relacionadas com anonimizac¸ ˜ao, confidencialidade e integridade s˜ao geralmente descartadas. Contudo, com o advento das plataformas cloud e a transic¸ ˜ao quer das aplicac¸ ˜oes quer dos seus logs para estes ecossistemas, processos de logging remotos, seguros e confidenciais surgem como um novo desafio. Adicionalmente, regulac¸ ˜ao como a RGPD, imp˜oe que as instituic¸ ˜oes e empresas garantam o armazenamento seguro dos dados. A forma mais comum de garantir a confidencialidade consiste na utilizac¸ ˜ao de t ´ecnicas criptogr ´aficas para cifrar a totalidade dos dados anteriormente `a sua transfer ˆencia para o servidor remoto. Caso sejam necess´ arias capacidades de pesquisa, a abordagem mais simples ´e a transfer ˆencia de todos os dados cifrados para o lado do cliente, que proceder´a `a sua decifra e pesquisa sobre os dados decifrados. Embora esta abordagem garanta a confidencialidade e privacidade dos dados, rapidamente se torna impratic ´avel com o crescimento normal dos registos de log. Adicionalmente, esta abordagem n˜ao faz uso do potencial total que a cloud tem para oferecer. Com base nesta tem´ atica, esta tese prop˜oe o desenvolvimento de uma soluc¸ ˜ao de armazenamento de logs operacionais de forma confidencial, integra e autˆ entica, fazendo uso das capacidades de armazenamento e computac¸ ˜ao das plataformas cloud. Adicionalmente, a possibilidade de pesquisa sobre os dados ´e mantida. Essa pesquisa ´e realizada server-side diretamente sobre os dados cifrados e sem acesso em momento algum a dados n˜ao cifrados por parte do servidor..

A parallelized disjunctive query based searchable encryption scheme for big data

Searchable Encryption (SE) allows a client to search over large amounts of encrypted data outsourced to the Cloud. Although, this helps to maintain the confidentiality of the outsourced data but achieving privacy is a difficult and resource intensive task. With the increase in the query effectiveness, i.e., by shifting from single keyword SE to multi-keyword SE there is a notable drop in the efficiency. This motivates to make use of the advances in the multi-core architectures and multiple threads where the search can be delegated across different threads to perform search in a parallel fashion. The proposed scheme is based on probabilistic trapdoors that are formed by making use of the property of modular inverses. The use of probabilistic trapdoors helps resist distinguishability attacks. The rigorous security analysis helps us to appreciate the advantage of having a probabilistic trapdoor. Furthermore, to validate the performance of the proposed scheme, it is implemented and deployed onto the British Telecommunication's Public Cloud offering and tested over a real speech corpus. The implementation is also extended to anticipate the performance gain by using the multi-core architecture that helps to maintain the lightweight property of the scheme

EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing

Edge computing is an extension of cloud computing that enables messages to be acquired and processed at low cost. Many terminal devices are being deployed in the edge network to sense and deal with the massive data. By migrating part of the computing tasks from the original cloud computing model to the edge device, the message is running on computing resources close to the data source. The edge computing model can effectively reduce the pressure on the cloud computing center and lower the network bandwidth consumption. However, the security and privacy issues in edge computing are worth noting. In this paper, we propose an efficient auto-correction retrieval scheme for data management in edge computing, named EARS-DM. With automatic error correction for the query keywords instead of similar words extension, EARS-DM can tolerate spelling mistakes and reduce the complexity of index storage space. By the combination of TF-IDF value of keywords and the syntactic weight of query keywords, keywords who are more important will obtain higher relevance scores. We construct an R-tree index building with the encrypted keywords and the children nodes of which are the encrypted identifier FID and Bloom filter BF of files who contain this keyword. The secure index will be uploaded to the edge computing and the search phrase will be performed by the edge computing which is close to the data source. Then EDs sort the matching encrypted file identifier FID by relevance scores and upload them to the cloud server (CS). Performance analysis with actual data indicated that our scheme is efficient and accurate