Outsourced Cloud Data Research Articles

Lightning-fast and privacy-preserving outsourced computation in the cloud

In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can securely achieve the outsource data storage and fast, secure data processing in a single cloud server different from the existing multi-server outsourced computation model. Specifically, we first present a general secure computation framework for LightCom under the cloud server equipped with multiple Trusted Processing Units (TPUs), which face the side-channel attack. Under the LightCom, we design two specified fast processing toolkits, which allow the user to achieve the commonly-used secure integer computation and secure floating-point computation against the side-channel information leakage of TPUs, respectively. Furthermore, our LightCom can also guarantee access pattern protection during the data processing and achieve private user information retrieve after the computation. We prove that the proposed LightCom can successfully achieve the goal of single cloud outsourced data processing to avoid the extra computation server and trusted computation server, and demonstrate the utility and the efficiency of LightCom using simulations.

Assure deletion supporting dynamic insertion for outsourced data in cloud computing

With the rapid development of cloud computing, an increasing number of data owners are willing to employ cloud storage service. In cloud storage, the resource-constraint data owners can outsource their large-scale data to the remote cloud server, by which they can greatly reduce local storage overhead and computation cost. Despite plenty of attractive advantages, cloud storage inevitably suffers from some new security challenges due to the separation of outsourced data ownership and its management, such as secure data insertion and deletion. The cloud server may maliciously reserve some data copies and return a wrong deletion result to cheat the data owner. Moreover, it is very difficult for the data owner to securely insert some new data blocks into the outsourced data set. To solve the above two problems, we adopt the primitive of Merkle sum hash tree to design a novel publicly verifiable cloud data deletion scheme, which can also simultaneously achieve provable data storage and dynamic data insertion. Moreover, an interesting property of our proposed scheme is that it can satisfy private and public verifiability without requiring any trusted third party. Furthermore, we formally prove that our proposed scheme not only can achieve the desired security properties, but also can realize the high efficiency and practicality.

Fully Homomorphic Symmetric Key Encryption-Based Access Control over Outsourced Cloud Data Using Smith Normal Form

Cloud computing is an innovative technology to support outsourcing of computing resources on-demand. But it is lack of security, confidentiality, and privacy. This paper attempts to propose a solution to the above-said problems by a secure, confidential and Fully Homomorphic Encryption technique with the desired level of access control. The present scheme is based on symmetric keys and Smith Normal Form assures the homomorphic property. All the operations are simple matrix multiplications and consequently, the present scheme sustains the owners of the cloud to secure their private data from its uncertain clients.

Privacy Preserving Data Mining With Multi Keyword Ranked Search for Medical Data

Privacy Preserving Data Mining (PPDM) maintains the privacy of data stored in cloud. This work aims to protect outsourced data in cloud, and also permit multi keyword search over the encrypted data in a secure way by NLP process without downloading and decrypting all files. Different methods for privacy preservation were analyzed and randomization for multilevel trust is proposed along with an efficient method for keyword search in cloud.

FSS-SDD: fuzzy-based semantic search for secure data discovery from outsourced cloud data

In current decade, cloud computing has become more prevalent and utilized for managing more sensitive data that are stored over cloud storage. While outsourcing the sensitive and private data on to the cloud, security is the major factor to be concentrated in efficient data search using keywords. As is auspicious, security of selective keyword search can be achieved using the powerful cryptographic technique called encryption. That is, the data that are to be outsourced have to be encrypted before storing it on cloud. Symmetric encryption is used between the data owner and the customer, whereas asymmetric encryption is used between the data owner and the cloud server. In such cases, the data discovery and retrieval have become more stimulating process. However, there are many methodologies developed for searching the encrypted outsourced data from the cloud. In this paper, the efficient data discovery process is focused and enhanced with the developed model called fuzzy-based semantic search for secure data discovery (FSS-SDD). A multi-valued logic called fuzzy logic is used, which has truth values of variables ranging between 0 and 1. It is used where the truth values range between completely true and completely false. The fuzzy logic-based semantic search improves the searching experience of the end user, by finding and retrieving the exact matching files for corresponding search files given by the user. Moreover, the model discovers the closely relevant matches using the semantic similarities, in cases, when the exact matches are not avail. For reducing the false positive rates, grading mechanism is enforced. Using the proposed FSS-SDD model, the processing overhead on new updates is effectively reduced and security in data retrieval is guaranteed.

Toward Highly Secure Yet Efficient KNN Classification Scheme on Outsourced Cloud Data

Nowadays, outsourcing data and machine learning tasks, e.g., $k$ -nearest neighbor (KNN) classification, to clouds has become a scalable and cost-effective way for large scale data storage, management, and processing. However, data security and privacy issue have been a serious concern in outsourcing data to clouds. In this article, we propose a privacy-preserving KNN classification scheme on cloud data in a twin-cloud model based on an additively homomorphic cryptosystem and secret sharing. Compared with existing works, we redesign a set of lightweight building blocks, such as secure square Euclidean distance, secure comparison, secure sorting, secure minimum, and maximum number finding, and secure frequency calculating, which achieve the same security level but with higher efficiency. In our scheme, data owners stay offline, which is different from secure-multiparty computation-based solutions which require data owners’ stay online during computation. In addition, query users do not interact with the cloud except sending query data and receiving the query results. Our security analysis shows that the scheme protects outsourced data security and query privacy, and hides access patterns. The experiments on real-world dataset indicate that our scheme is significantly more efficient than existing schemes.

Secured Fine-Grained Selective Access to Outsourced Cloud Data in IoT Environments

With the vast increase in data transmission due to a large number of information collected by devices, data management, and security has been a challenge for organizations. Many data owners (DOs) outsource their data to cloud repositories due to several economic advantages cloud service providers present. However, DOs, after their data are outsourced, do not have complete control of the data, and therefore, external systems are incorporated to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data but prove to be deficient in providing suitable solutions to the problem. In this article, we propose a secure fine-grain access control system for outsourced data, which supports read and write operations to the data. We make use of an attribute-based encryption (ABE) scheme, which is regarded as a suitable scheme to achieve access control for security and privacy (confidentiality) of outsourced data. This article considers different categories of data users, and make provisions for distinct access roles and permissible actions on the outsourced data with dynamic and efficient policy updates to the corresponding ciphertext in cloud repositories. We adopt blockchain technologies to enhance traceability and visibility to enable control over outsourced data by a DO. The security analysis presented demonstrates that the security properties of the system are not compromised. Results based on extensive experiments illustrate the efficiency and scalability of our system.

A privacy-preserving multi-keyword search approach in cloud computing

Cloud computing provides the users with the ability to outsource their data to a third-party cloud storage for cost-effective management of resources and on-demand network access. However, outsourcing the data to a third-party location may raise concerns about data privacy. To maintain the user’s privacy, users tend to encrypt their sensitive data before outsourcing it. Encrypting the data will preserve its privacy, but at the same time, it makes the searching process for a specific keyword a time-consuming and challenging process, mainly if the encryption key is not provided. On the other hand, the data owner should be able to perform multiple keyword searches to retrieve specific documents that are relevant to the search query. This paper proposes a new privacy-preserving multi-keyword search approach for the cloud outsourced data. The objective of the proposed approach is to allow the data owners and the authorized users to retrieve the most relevant data with minimum computation and communication overhead, and reduced false positives (irrelevant documents) and searching time. To evaluate the proposed approach, the NSF research dataset is used. Results demonstrate that the proposed method achieves better searching time and overall performance of the cloud environment regarding computation and communication overhead as well as false positives in comparison with other approaches.

Integrity and Confidentiality in Cloud Outsourced Data

Abstract Cloud services have become an increasingly popular solution to provide different services to clients. One of the cloud services is database as a service (DBaaS), in which the service provider offers different resources such as software, hardware and network to the clients to be able to manage and administer the database. However, the data and the execution of database queries are under control of the distrustful service provider. This lack of trust opens up new security issues and serves as the chief motivation of our work. This study presents an overview of different cryptographic algorithms, based on different schemes in the outsourced database security and query authentication. We conclude the paper by proposing a new architecture that achieves the confidentiality and integrity of query results of the outsourced database.

A Novel Securable Fuzzy Logic Based Ranking Scheme for Document Searching on Outsourced Cloud Data

Most of the existing searchable encryption scheme allows the user to search for the encrypted data with the support of Boolean search and this scheme are not so much effective to meet the data requirements of the user in the presence of large amount of documents in cloud. In this paper, we propose an effective Securable Fuzzy Logic based Ranking mechanism for document searching on outsourced cloud data. Our approach employs ECC based encryption and a fuzzy logic based ranking scheme over the encrypted-data to retrieve the documents from the cloud. The newly developed fuzzy logic based ranking scheme adopts six query-expansion (QE) ‘terms selection’ methods for computing the degrees of all the unique-terms comprised in the top retrieved document. Further, our fuzzy logic based ranking mechanism greatly enhances the system functionality by sending top-most relevant documents based on the relevance scores obtained for the term selection methods and increases the document retrieval accuracy by sending alone the top-most relevant documents instead of transmitting all documents back. As a result, data security is increased by reducing the communication and computational overhead. The experimental validations are performed on RFC and FIRE dataset. Through experimental analysis, we prove that our proposed approach is highly secure and efficient as well as exhibits better recall and precision rate in the IR system to deal with the document-retrieval process.

Genuine and secure public auditing for the outsourced data in cloud

In cloud storage, the most common concerns for users are data integrity, confidentiality and availability. Recently, many data integrity auditing schemes have been proposed, some of which achieved privacy-preserving public auditing, some of which realised data sharing and group dynamic, and some of which supported data dynamic. However, as far as we know, there does not exist a practical auditing scheme which can simultaneously realise all the functions mentioned above; In addition, all the existing schemes adopt the method of computing message authentication code (MAC) by users to achieve the following data integrity auditing, nevertheless, it is a arduous task to compute MACs for these cloud users with constrained computing and storage capabilities. In this paper, we propose a novel privacy-preserving public auditing scheme for shared data in the cloud, which can also support data dynamic operation and group dynamic. First, we bring proxy signature into the existing auditing scheme. The cloud user can delegate the computation of MACs to a cloud computing server, so that the user's burden would be greatly reduced; second, by introducing a Lagrange interpolating polynomial, our scheme realises the identity privacy-preserving in the precondition of almost no addition of any new computation and less storage space, moreover it makes group dynamic simple; third, we make an index-switch table and combine it with the Merkle hash tree to realise the practical and secure dynamic operations of shared data by group users; fourth, in our scheme, the cloud storage server will add its private key in producing the proof information to protect the data privacy and resist the active attack. Theoretical analysis demonstrates our scheme is provably secure.

ERAC-MAC efficient revocable access control for multi-authority cloud storage system

In the current scenario, there is an appreciable escalation in the utilisation of cloud computing by critical industrial applications due to its cost-effective storage and computing. However, due to an untrusted server in a cloud, the access control turns out to be the challenging issue to ensure the confidentiality of sensitive data. The ciphertext policy attribute-based encryption (CP-ABE) is deliberated to be an apt technique to enforce the access control for encrypted cloud outsourced data. But, due to the computation complexity of decryption, user revocation and complexity of key management for achieving granularity, prevailing CP-ABE schemes when applied directly to multi authority attribute system, incurs more computational costs in the order of NP. In this paper, an efficient CP-ABE based multi authority attribute system is put forth that supports decryption and user revocation by CSP with the advent of a blind encryption/decryption technique and a novel colouring scheme for predicting user behaviour analysis. Security and performance of ERAC-MAC was analysed and found to be much better than the other prevailing schemes. The implementation was done using the paring based cryptography library of the Stanford University in Ubuntu environment.

ERAC-MAC efficient revocable access control for multi-authority cloud storage system

In the current scenario, there is an appreciable escalation in the utilisation of cloud computing by critical industrial applications due to its cost-effective storage and computing. However, due to an untrusted server in a cloud, the access control turns out to be the challenging issue to ensure the confidentiality of sensitive data. The ciphertext policy attribute-based encryption (CP-ABE) is deliberated to be an apt technique to enforce the access control for encrypted cloud outsourced data. But, due to the computation complexity of decryption, user revocation and complexity of key management for achieving granularity, prevailing CP-ABE schemes when applied directly to multi authority attribute system, incurs more computational costs in the order of NP. In this paper, an efficient CP-ABE based multi authority attribute system is put forth that supports decryption and user revocation by CSP with the advent of a blind encryption/decryption technique and a novel colouring scheme for predicting user behaviour analysis. Security and performance of ERAC-MAC was analysed and found to be much better than the other prevailing schemes. The implementation was done using the paring based cryptography library of the Stanford University in Ubuntu environment.

PPK-Means: Achieving Privacy-Preserving Clustering Over Encrypted Multi-Dimensional Cloud Data

Clustering is a fundamental and critical data mining branch that has been widely used in practical applications such as user purchase model analysis, image color segmentation, outlier detection, and so on. With the increasing popularity of cloud computing, more and more encrypted data are converging to cloud computing platforms for enjoying the revolutionary advantages of the cloud computing paradigm, as well as mitigating the deeply concerned data privacy issues. However, traditional data encryption makes existing clustering schemes no more effective, which greatly obstructs effective data utilization and frustrates the wide adoption of cloud computing. In this paper, we focus on solving the clustering problem over encrypted cloud data. In particular, we propose a privacy-preserving k-means clustering technology over encrypted multi-dimensional cloud data by leveraging the scalar-product-preserving encryption primitive, called PPK-means. The proposed technique is able to achieve efficient multi-dimensional data clustering as well to preserve the confidentiality of the outsourced cloud data. To the best of our knowledge, our work is the first to explore the privacy-preserving multi-dimensional data clustering in the cloud computing environment. Extensive experiments in simulation data-sets and real-life data-sets demonstrate that our proposed PPK-means is secure, efficient, and practical.

Improved secure fuzzy auditing protocol for cloud data storage

Nowadays, more and more people prefer to outsource their storage to the cloud; however, due to some accidents, cloud storage service providers may lose some data outsourced by the data owners. Thus a mechanism to ensure the outsourced cloud data remaining intact is needed for smoothly running the cloud storage service. Fuzzy cloud auditing protocol is such a mechanism running between data owners and cloud storage service providers. In these protocols, the data owner fuzzy challenges the cloud storage servers on the randomly chosen data blocks with random values, the servers need to response with corrected aggregated tag proof to pass through the auditing process. Until now, there are many fuzzy cloud auditing protocols with various interesting properties. In 2015, Yuan et al. proposed an auditing scheme supporting publicly integrity checking and dynamic data sharing with multi-user modification, which aims at allowing multiple cloud users to modify data while ensuring the cloud data’s integrity. Also recently Yuan et al. proposed a public proofs of retrievability (POR) in cloud with constant cost, they showed their scheme is the first POR scheme which can simultaneously achieve public verifiability, constant communication and computational costs on users, and prove the security of their scheme. However, in this paper, we show their schemes are not secure, concretely, the tags in their schemes can be easily forged. We also give an improved fuzzy cloud auditing scheme for the data owners.

Verifiable Search Over Updatable Encrypted Data in Cloud Computing

With all the benefits from cloud computing, there are negative influences for the data trust and integrity since clients lose control over the outsourced data in clouds. We propose a verification scheme that supports keywords based search among the encrypted data which is updatable. During the verification process the outsourced cloud data are protected from being inferred by the cloud server. Additionally, if the cloud server returns wrong or incomplete search results the clients will be able to detect such failures. A novel concept in our scheme is the ability of clients to update their outsourced data and to ensure the data’s correctness. With our scheme, the data’s update efficiency is high and the client’s computational cost is low, which makes our scheme very suitable for resource constrained devices.

Anonymous Data Sharing Scheme in Public Cloud and Its Application in E-Health Record

In the past few years, cloud computing develops very quickly. A large amount of data are uploaded and stored in remote public cloud servers which cannot fully be trusted by users. Especially, more and more enterprises would like to manage their data by the aid of the cloud servers. However, when the data outsourced in the cloud are sensitive, the challenges of security and privacy becomes urgent for wide deployment of the cloud systems. This paper proposes a secure data sharing scheme to ensure the privacy of data owner and the security of the outsourced cloud data. The proposed scheme provides flexible utility of data while solving the privacy and security challenges for data sharing. The security and efficiency analysis demonstrate that the designed scheme is feasible and efficient. At last, we discuss its application in electronic health record.

Secure multi-keyword ranked search over encrypted cloud data for multiple data owners

Secure multi-keyword ranked search over outsourced cloud data has become a hot research field. Most existing works follow the model of “Single Owner”, which just supports searching on the outsourced data belong to only one data owner. But the more realistic scenario is “Multiple Owners”, users could search on all datasets outsourced by different data owners. However, directly extending “Single Owner” schemes into “Multiple Owners” scenario still face the major two challenges: (1) the inconvenient key management and the resulting high communication cost; (2) due to the different authorities of owners, the qualities of documents are also different even if they are about the similar topic, but current rank functions in this area cannot rank documents based on their qualities. In this paper, we propose a secure multi-keyword ranked search scheme for multiple data owners. A trusted third party is imported to solve the problem of key management. We exploit the vector space model for generating index and query, and our new-designed KDO algorithm is utilized for providing keyword weight, so that the rank function not only considers about the relevance between query and document, but also takes into account the document quality. In order to protect privacy for both owners and users, the Asymmetric Scalar-product Preserving Encryption approach is utilized for encrypting weighted index and query. Besides, we construct the Grouped Balanced Binary tree index, which could further improve the search efficiency by Greedy Depth-first search algorithm. Extensive experiments demonstrate that our proposed scheme is secure, accurate and efficient.

Accountable CP-ABE with Public Verifiability: How to Effectively Protect the Outsourced Data in Cloud

Ciphertext-policy attribute-based encryption, denoted by CP-ABE, extends identity based encryption by taking a set of attributes as users’ public key which enables scalable access control over outsourced data in cloud storage services. However, a decryption key corresponding to an attribute set may be owned by multiple users. Then, malicious users are subjectively willing to share their decryption keys for profits. In addition, the authority who issues decryption keys in CP-ABE system is able to generate arbitrary decryption key for any (including unauthorized) user. Key abuses of both malicious users and the authority have been regarded as one of the major obstacles to deploy CP-ABE system in real-world commercial applications. In this paper, we try to solve these two kinds of key abuses in CP-ABE system, and propose two accountable CP-ABE schemes supporting any LSSS realizable access structures. Two proposed accountable CP-ABE schemes allow any third party (with the help of authorities if necessary) to publicly verify the identity of an exposed decryption key, allow an auditor to publicly audit whether a malicious user or authorities should be responsible for an exposed decryption key, and the key abuser can’t deny it. At last, we prove the two schemes can achieve publicly verifiable traceability and accountability.

Privacy-Preserving Multikeyword Similarity Search Over Outsourced Cloud Data

The amount of data generated by individuals and enterprises is rapidly increasing. With the emerging cloud computing paradigm, the data and corresponding complex management tasks can be outsourced to the cloud for the management flexibility and cost savings. Unfortunately, as the data could be sensitive, the direct data outsourcing would have the problem of privacy leakage. The encryption can be used, before the data outsourcing, with the concern that the operations can still be accomplished by the cloud. We consider the multikeyword similarity search over outsourced cloud data. In particular, with the consideration of the text data only, multiple keywords are specified by the user. The cloud returns the files containing more than a threshold number of input keywords or similar keywords, where the similarity here is defined according to the edit distance metric. We propose three solutions, where blind signature provides the user access privacy, and a novel use of Bloom filter's bit pattern provides the speedup of search task at the cloud side. Our final design to achieve the search is secure against insider threats and efficient in terms of the search time at the cloud side. Performance evaluation and analysis are used to demonstrate the practicality of our proposed solutions.