Biblio

With increasing popularity of cloud computing, the data owners are motivated to outsource their sensitive data to cloud servers for flexibility and reduced cost in data management. However, privacy is a big concern for outsourcing data to the cloud. The data owners typically encrypt documents before outsourcing for privacy-preserving. As the volume of data is increasing at a dramatic rate, it is essential to develop an efficient and reliable ciphertext search techniques, so that data owners can easily access and update cloud data. In this paper, we propose a privacy preserving multi-keyword ranked search scheme over encrypted data in cloud along with data integrity using a new authenticated data structure MIR-tree. The MIR-tree based index with including the combination of widely used vector space model and TF×IDF model in the index construction and query generation. We use inverted file index for storing word-digest, which provides efficient and fast relevance between the query and cloud data. Design an authentication set(AS) for authenticating the queries, for verifying top-k search results. Because of tree based index, our scheme achieves optimal search efficiency and reduces communication overhead for verifying the search results. The analysis shows security and efficiency of our scheme.

Cloud computing, often referred to as simply “the cloud,” is the delivery of on-demand computing resources; everything from applications to data centers over the Internet. Cloud is used not only for storing data, but also the stored data can be shared by multiple users. Due to this, the integrity of cloud data is subject to doubt. Every time it is not possible for user to download all data and verify integrity, so proposed system contain Third Party Auditor (TPA) to verify the integrity of shared data. During auditing, the shared data is kept private from public verifiers, who are able to verify shared data integrity without downloading or retrieving the entire data file. Group signature is used to preserve identity privacy of group members from third party auditor. Privacy preserving is done to ensure that the TPA cannot derive user's data content from the information collected during the auditing process.

Cloud computing enables the outsourcing of big data analytics, where a third-party server is responsible for data management and processing. In this paper, we consider the outsourcing model in which a third-party server provides record matching as a service. In particular, given a target record, the service provider returns all records from the outsourced dataset that match the target according to specific distance metrics. Identifying matching records in databases plays an important role in information integration and entity resolution. A major security concern of this outsourcing paradigm is whether the service provider returns the correct record matching results. To solve the problem, we design EARRING, an Efficient Authentication of outsouRced Record matchING framework. EARRING requires the service provider to construct the verification object (VO) of the record matching results. From the VO, the client is able to catch any incorrect result with cheap computational cost. Experiment results on real-world datasets demonstrate the efficiency of EARRING.

The digital forensics refers to the application of scientific techniques in investigation of a crime, specifically to identify or validate involvement of some suspect in an activity leading towards that crime. Network forensics particularly deals with the monitoring of network traffic with an aim to trace some suspected activity from normal traffic or to identify some abnormal pattern in the traffic that may give clue towards some attack. Network forensics, quite valuable phenomenon in investigation process, presents certain challenges including problems in accessing network devices of cloud architecture, handling large amount network traffic, and rigorous processing required to analyse the huge volume of data, of which large proportion may prove to be irrelevant later on. Cloud Computing technology offers services to its clients remotely from a shared pool of resources, as per clients customized requirement, any time, from anywhere. Cloud Computing has attained tremendous popularity recently, leading to its vast and rapid deployment, however Privacy and Security concerns have also increased in same ratio, since data and application is outsourced to a third party. Security concerns about cloud architecture have come up as the prime barrier hindering the major shift of industry towards cloud model, despite significant advantages of cloud architecture. Cloud computing architecture presents aggravated and specific challenges in the network forensics. In this paper, I have reviewed challenges and issues faced in conducting network forensics particularly in the cloud computing environment. The study covers limitations that a network forensic expert may confront during investigation in cloud environment. I have categorized challenges presented to network forensics in cloud computing into various groups. Challenges in each group can be handled appropriately by either Forensic experts, Cloud service providers or Forensic tools whereas leftover challenges are declared as be- ond the control.

So far, cloud storage has been accepted by an increasing number of people, which is not a fresh notion any more. It brings cloud users a lot of conveniences, such as the relief of local storage and location independent access. Nevertheless, the correctness and completeness as well as the privacy of outsourced data are what worry could users. As a result, most people are unwilling to store data in the cloud, in case that the sensitive information concerning something important is disclosed. Only when people feel worry-free, can they accept cloud storage more easily. Certainly, many experts have taken this problem into consideration, and tried to solve it. In this paper, we survey the solutions to the problems concerning auditing in cloud computing and give a comparison of them. The methods and performances as well as the pros and cons are discussed for the state-of-the-art auditing protocols.

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.

This paper presents on-going research to define the basic models and architecture patterns for federated access control in heterogeneous (multi-provider) multi-cloud and inter-cloud environment. The proposed research contributes to the further definition of Intercloud Federation Framework (ICFF) which is a part of the general Intercloud Architecture Framework (ICAF) proposed by authors in earlier works. ICFF attempts to address the interoperability and integration issues in provisioning on-demand multi-provider multi-domain heterogeneous cloud infrastructure services. The paper describes the major inter-cloud federation scenarios that in general involve two types of federations: customer-side federation that includes federation between cloud based services and customer campus or enterprise infrastructure, and provider-side federation that is created by a group of cloud providers to outsource or broker their resources when provisioning services to customers. The proposed federated access control model uses Federated Identity Management (FIDM) model that can be also supported by the trusted third party entities such as Cloud Service Broker (CSB) and/or trust broker to establish dynamic trust relations between entities without previously existing trust. The research analyses different federated identity management scenarios, defines the basic architecture patterns and the main components of the distributed federated multi-domain Authentication and Authorisation infrastructure.

Outsourcing spatial databases to the cloud provides an economical and flexible way for data owners to deliver spatial data to users of location-based services. However, in the database outsourcing paradigm, the third-party service provider is not always trustworthy, therefore, ensuring spatial query integrity is critical. In this paper, we propose an efficient road network k-nearest-neighbor query verification technique which utilizes the network Voronoi diagram and neighbors to prove the integrity of query results. Unlike previous work that verifies k-nearest-neighbor results in the Euclidean space, our approach needs to verify both the distances and the shortest paths from the query point to its kNN results on the road network. We evaluate our approach on real-world road networks together with both real and synthetic points of interest datasets. Our experiments run on Google Android mobile devices which communicate with the service provider through wireless connections. The experiment results show that our approach leads to compact verification objects (VO) and the verification algorithm on mobile devices is efficient, especially for queries with low selectivity.

Recently, cloud computing has been spotlighted as a new paradigm of database management system. In this environment, databases are outsourced and deployed on a service provider in order to reduce cost for data storage and maintenance. However, the service provider might be untrusted so that the two issues of data security, including data confidentiality and query result integrity, become major concerns for users. Existing bucket-based data authentication methods have problem that the original spatial data distribution can be disclosed from data authentication index due to the unsophisticated data grouping strategies. In addition, the transmission overhead of verification object is high. In this paper, we propose a privacy-aware query authentication which guarantees data confidentiality and query result integrity for users. A periodic function-based data grouping scheme is designed to privately partition a spatial database into small groups for generating a signature of each group. The group signature is used to check the correctness and completeness of outsourced data when answering a range query to users. Through performance evaluation, it is shown that proposed method outperforms the existing method in terms of range query processing time up to 3 times.