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Survey on Implementing Privacy Preserving Model for Shared Data in The Cloud

Survey on Implementing Privacy Preserving Model for Shared Data in The Cloud

In this paper Cong Wang , Qian Wang etl. proposed the Homomorphic linear authenticator with random masking for preserving the privacy of public auditing system for shared data in the cloud , which not only release burden of shared data but also avoid users fear of data leakage when sharing data on cloud. In public key homomorphic linear authenticator Third party auditor can performed the auditing task without demanding the local copy of original data so it reduce the computation and communication overhead rather that other auditing approaches. They proposed their experiment conducted on Amazon EC2 instance.
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Privacy-Preserving Public Auditingfor Shared data With Efficient User Revocation

Privacy-Preserving Public Auditingfor Shared data With Efficient User Revocation

Abstract- Users in a particular group need to compute signatures on the blocks in shared data,so that the shared data integrity can be confirmed publicly,.Various blocks in shared data are usually signed by various vast number of users due to data alterations performed by different users. Once a user is revoked from the group, an existing user must resign the data blocks of the revoked user in order to ensure the security of data. Due to the massive size of shared data in the cloud, the usual process, which permits an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient. With our mechanism, the identity of the signer on each block in shared data is kept private from public verifiers, who are able to efficiently verify shared data integrity without retrieving the entire file. In addition, our mechanism is able to perform multiple auditing tasks simultaneously instead of verifying them one by one. Our experimental results demonstrate the effectiveness and efficiency of our mechanism when auditing shared data integrity.
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Providing security for the Encrypted shared data in the cloud Environment

Providing security for the Encrypted shared data in the cloud Environment

In the gift system a secure multi-keyword class-conscious search theme over encrypted cloud data, that at a similar time supports dynamic update operations like deletion and insertion of documents. Specifically, the vector house model and conjointly the widely-used TF_IDF model unit combined among the index construction and question generation. we tend to tend to construct a special tree-based index structure and propose a “Greedy Depth-first Search” rule to provide economical multi-keyword class-conscious search. The secure kNN rule is employed to inscribe the index and question vectors, and within the meanwhile guarantee correct association score calculation between encrypted index and question vectors. Therefore on resist math attacks, phantom terms unit supplementary to the index vector for bright search results. Attributable to the utilization of our special tree-based index structure, the planned theme will do sub-linear search time and upset the deletion and insertion of documents flexibly. Intensive experiments unit conducted to demonstrate the efficiency of the planned theme. Among the planned system we tend to tend to propose the first privacy-preserving mechanism that allows public auditing on shared data keep among the cloud. Specially, we tend to tend to take advantage of ring signatures to calculate the verification knowledge needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is unbroken personal from a third-party auditor (TPA), United Nations agency continues to be able to publically verify the integrity of shared data whereas not retrieving the full file. Our experimental results demonstrate the effectiveness and efficiency of our planned mechanism once auditing shared data.
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Efficient User Revocation for Shared Data in the Public Cloud

Efficient User Revocation for Shared Data in the Public Cloud

Thus here we come to conclude that our system have an ability to generate a fully unique public auditing mechanism through revocation of economical user for integrity of shared data. Propose system aims to enable the cloud to automatically re-sign data blocks through existing users while creating the proxy re-signatures. There is no need of user to re-sign blocks manually. Public verifier is able to audit the integrity of data being shared and does not retrieve the complete data, but some part of data shared are re-signed by cloud itself. This system enables batch auditing by examining multiple tasks in synchronous way. Her we are allowing semi-trusted cloud to verify and re-sign blocks using proxy signatures at the time of user revocation.
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PRIVACY PRESERVING PUBLIC AUDITING FOR SHARED DATA IN THE CLOUD

PRIVACY PRESERVING PUBLIC AUDITING FOR SHARED DATA IN THE CLOUD

As future enhancement, we enhance the Oruta system in two interesting problems we will continue to study for our future work. One of them is traceability, which means the ability for the group manager (i.e., the original user) to reveal the identity of the signer based on verification metadata in some special situations. Since Oruta is based on ring signatures, where the identity of the signer is unconditionally protected, the current design of ours does not support traceability. To the best of our knowledge, designing an efficient public auditing mechanism with the capabilities of preserving identity privacy and supporting traceability is still open. Another problem for our future work is how to prove data freshness (prove the cloud possesses the latest version of shared data) while still preserving identity privacy.
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SURVEY ONPRIVACY PRESERVING PUBLIC AUDITING MECHANISM FOR SHARED DATA IN CLOUD COMPUTING ENVIRONMENT

SURVEY ONPRIVACY PRESERVING PUBLIC AUDITING MECHANISM FOR SHARED DATA IN CLOUD COMPUTING ENVIRONMENT

In [2], author describes about the security challenges cloud computing presents the burden of local data storage and maintenance. Public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor to check the integrity of shared data. The advantage of this mechanism is it eliminates the burden of cloud user from the tedious and possibly expensive auditing task.
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Group User Revocation in Cloud for Shared Data

Group User Revocation in Cloud for Shared Data

The outsourced data is usually stored in encrypted database, in previous research. This schema is designed for auditing of both plaintext and cipher text database. This is support for encrypted database. The group consist of only one user that is data owner, then only need to choose random secrete key And encrypt the data using encryption. when it needs to support the multiuser data modification, then it is difficult to keep the shared data for encryption, so that the single point can share a secrete key among the number of user. But there is chance of leakage of shared secrete key which break the shared data. So to avoid this problem, we use scheme, which supports multi-user group modification.
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PROTECTION OF SHARED DATA FOR ONLINE SOCIAL NETWORKS

PROTECTION OF SHARED DATA FOR ONLINE SOCIAL NETWORKS

case, when a user uploads an image and tags friends who become visible in the photo, the tagged friends cannot check who can observe this photo, even though the tagged friends may have dissimilar privacy concerns about the photo. To take in hand such a serious issue, preface protection mechanisms have been offered by existing OSNs. Suppose Facebook allows tagged users to remove the tags linked to their profiles or report violations asking Facebook supervisors to remove the contents that they do not want to share with the public. These simple protection mechanisms suffer from several boundaries. On one hand, removing a tag from a photo can only avoid other members from seeing a user’s profile by means of the association link, but the user’s image is still enclosed in the photo. Since innovative access control policies cannot be distorted, the user’s image continues to be exposed to all authorized users and reporting to OSNs only allows us to either keep or remove the content. Such a binary decision from OSN managers is either too loose or too preventive, relying on the OSN’s administration and requiring several people to report their request on the same content. Therefore, it is necessary to develop an effective and flexible access control mechanism for OSNs, accepting the special authorization requirements coming from multiple associated users for managing the shared data collaboratively.
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Improving Security Techniques for Shared Data in Cloud Computing

Improving Security Techniques for Shared Data in Cloud Computing

In the social networking sites there are large number of data in datasets. There are big challenges to manage the data and analyse the data which is more important or not. There are big target how to secure a data. There are different types of data for sharing. In these data’s some data’s are very common and general which does not effect on the privacy but some data are very confidential which should not shared to every people .During in this situation when the confidential data are shared it can be misused by another people. So it is necessary that there should be some restriction apply to social networking sites for data security. One of them is privacy. In these datasets there are also a shared data. So presently due to no restriction, any person who are associated with social networking sites can share any types of data to one another. There should be data uses authority so that only authorized person can share the important data .There should be validation requirement so that unauthorized person cannot access or share the data. It is very necessary thing to secure the data of social networking sites and also the value of social networking sites.
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Encrypted Shared Data Spaces

Encrypted Shared Data Spaces

The shared data space model was introduced by the coordination language Linda [8]. Linda provides three basic operations: out , in and rd . The out operation inserts a tuple into the tuple space. The in and rd operations respectively take (destruc- tive) and read (non-destructive) a tuple from the tuple space, using a template for matching. The tuple returned must exactly match every value of the template. Templates may contain wildcards, which match any value. Whereas putting a tu- ple inside the tuple space is non-blocking (i.e. the process that puts the tuple re- turns immediately from the call to out ), reading and taking from the tuple space is blocking: the call returns only when a matching tuple is found. In the original model two more operations were introduced: the inp and rdp . These operations are predicate versions of in and rd : they too try to return a matching tuple. However, if there is no such tuple they do not block but return a value indicating failure.
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Provide Privacy of Shared Data and Calculate Performance of Data Management System in Multiple Clouds

Provide Privacy of Shared Data and Calculate Performance of Data Management System in Multiple Clouds

So in this paper we are proposed a novel multi authority and efficient cryptography techniques for authentication in the cloud. Before stored data into cloud server each user will be identify by the cloud server. After completion of authentication process the cloud server will generate master key or first level encryption key for data encryption and decryption. Before performing encryption and decryption process the cloud server will send second level key all user mail ids. By using second level each user will get first level encryption key. The cloud server also sends first level encryption key or master key to data owner. The data owner will take first level encryption key and perform the encryption process. In this paper we are using extended tiny encryption algorithm for performing encryption process. After completion of encryption process the cloud server will stored cipher formatted data into cloud server. The authenticated user will take second level key and get the first level encryption key. By using first level encryption key or master will perform the decryption process of extended tiny encryption algorithm. After completion of decryption process each user will get plain format data. In this paper we can also implement AES algorithm for doing encryption and decryption of data. After completion of encryption and decryption process we can calculate time of those algorithms. Taking that time we can evaluate performance of those algorithms. After completion of performance evaluation process the proposed algorithm give the better performance other the AES algorithm. By performing those operations we can improve privacy in the shared data and provide more efficiency in the authentication, key generation process.
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Secure Cloud Auditor for Efficient Data De-duplication with maintaining shared data Integrity

Secure Cloud Auditor for Efficient Data De-duplication with maintaining shared data Integrity

Integrity is simply defined as consistency. Integrity is one of the security factors which influence the cloud performance. Data integrity defines rules for writing data in a reliable manner to keep persistent data storages. This phenomenon helps to retrieved data as it is without any changes. Preserving integrity of shared data is tough task. There are various mechanisms have been recommended [2]-[7] to preserve integrity of data. Integrity is most important security for cloud data storages because it ensure about completeness of data also provide detail information that available data is correct, easily accessible to authorized users only, data is consistent and of high quality.
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Shared Data Science Infrastructure for Genomics Data

Shared Data Science Infrastructure for Genomics Data

Creating a computational infrastructure to analyze the wealth of information contained in data repositories that scales well is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared Data Science Infrastructures like Boa can be used to more efficiently process and parse data contained in large data repositories. The main features of Boa are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories. Here, we present an implementation of Boa for Genomic research (BoaG) on a relatively small data repository: RefSeq's 97,716 annotation (GFF) and assembly (FASTA) files and metadata. We used BoaG to query the entire RefSeq dataset and gain insight into the RefSeq genome assemblies and gene model annotations and show that assembly quality using the same assembler varies depending on species. In order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, BoaG, can provide greater access to researchers to efficiently explore data in ways previously not possible for anyone but the most well funded research groups. We demonstrate the efficiency of BoaG to explore the RefSeq database of genome assemblies and annotations to identify interesting features of gene annotation as a proof of concept for much larger datasets.
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A SURVEY ON USER REVOCATION IN THE PUBLIC CLOUD FOR SHARED DATA

A SURVEY ON USER REVOCATION IN THE PUBLIC CLOUD FOR SHARED DATA

With shared information, once a client adjusts a square, she likewise needs to figure another mark for the altered square. Because of the adjustments from diverse clients, distinctive squares are marked by diverse clients. For security reasons, when a client leaves the gathering or acts mischievously, this client must be repudiated from the gathering. Accordingly, this repudiated client ought to never again have the capacity to get to and change shared information, and the marks produced by this renounced client are no more substantial to the gathering [6]. In this way, despite the fact that the substance of shared information is not changed amid client renouncement, the squares, which were already marked by the renounced client, still should be re-marked by a current client in the gathering.
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Enhanced Privacy Preserving with Data Freshness by Accomplishing Traceability over Oruta

Enhanced Privacy Preserving with Data Freshness by Accomplishing Traceability over Oruta

Abstract— 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. Several mechanisms have been designed to support public auditing on shared data stored in the cloud. During auditing, the shared data is kept private from public verifiers, who are able to verify shared data integrity using ring signature without downloading or retrieving the entire file. Ring signature is used to compute verification metadata needed to audit the correctness of shared data. With this, the identity of the signer in shared data is kept private from public verifiers. In this paper, we propose a traceability mechanism that improves Data Privacy by achieving traceability and the data freshness(the cloud possess the latest version of shared data) is also proved while still preserving identity privacy.
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Shared and searchable encrypted data for untrusted servers

Shared and searchable encrypted data for untrusted servers

In this work, we propose a scheme for multi-user searchable data encryption based on proxy cryptography. We consider the application scenario where a group of users share data through an untrusted data storage server which is hosted by a third party. Unlike existing schemes for searchable data encryption in multi-user settings which have constraints such as asymmetric user permis- sions (multiple writers, single reader) or read-only shared data set, in our scheme the shared data set can be updated by the users and each user in the group can be both reader and writer. The server can search on the encrypted data using encrypted keywords. More importantly our scheme do not rely on shared keys. This significantly simplifies key revocation. Each authorised user in the system has his own unique key set and can insert encrypted data, decrypt the data inserted by other users and search encrypted data without knowing the other users’ keys. The keys of one user can easily be revoked without affecting other users or the encrypted data at the server. After a user’s keys have been revoked, the user will no longer be able to read and search the shared data.
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Multiple Users Cloud Integrity by Public Auditing and Re-signed proxy-signature using third party auditor viva Public verifiers

Multiple Users Cloud Integrity by Public Auditing and Re-signed proxy-signature using third party auditor viva Public verifiers

In this current research work, this system sanctions Blocking Utilizer account and There is need to Authenticate with secret key in each time .There is need to we proposed an incipient public auditing[1] mechanism for shared data with efficient utilizer revocation in the cloud. When a utilizer in the group is revoked, we sanction the semitrusted cloud to re-sign blocks that were signed by the revoked utilizer with proxy re-signatures. Experimental results show that the cloud can amend the efficiency of utilizer revocation, and subsisting users in the group can preserve a consequential amount of computation and communication resources during utilizer revocation.
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Maintaining Integrity and Security for the Data Shared in the Cloud

Maintaining Integrity and Security for the Data Shared in the Cloud

In [2] authors proposed a method called Provable Data Possession (PDP) which allowed a public verifier to check the correctness of data which was being stored by the user or a client on an untrusted server. Even though, it offered high privacy for data of the user, it was good for only the static data. An extension to the PDP was introduced in [5]. In this extension model, authors implemented PDP using some symmetric keys which could provide support for the dynamic data. But it couldn’t do much for verifying the integrity of data as verifier could only provide limited number of verification request. Later, introduced the Merkel Hash Tree for supporting the public auditing mechanism by providing a complete support for fully dynamic operations.Users or clients who share the data on a storage space were so much worried about how to maintain the integrity of data, as the data became larger and larger the idea of checking the integrity of data by users itself need to get changed and authors suggested the idea to bring the Third Party Auditor (TPA) in [3] to overcome the workload or complexity felt by the users or clients to a greater extent. But protecting the private or confidential data of users from TPA came forward as an issue, but Wang solved it in a better way by random masking. In [8] authors proposed a model “Oruta” which could help in identifying the each of the signers who have signed on the data blocks being shared in that storage space and keep the signer’s identity private form the public verifiers and thus provide integrity of shared data without retrieving the entire file. Apart from the other previously discussed mechanisms this could perform multiple auditing tasks. And in [9] authors proposed another model called “Knox”. Even if there is large number of users, it is not affecting the auditing of large amounts of data shared by a client
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Data craft: integrating data into daily practices and shared reflections

Data craft: integrating data into daily practices and shared reflections

construction of meaning and identity through personal and joint reminiscing [2]. Access to unprecedented amounts of data about people's lives provides a potentially rich resource for reminiscing. However, sharing such data with others and deriving meaning from it is still a challenge [11]. For example, while people sometimes print digital photos, giving quantitative data (e.g., communication or location histories) a physical presence at home and making it accessible to all family members is more difficult. Previous research has highlighted integration with everyday practices and discoverability [5,8,9],
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Best practice for analysis of shared clinical trial data

Best practice for analysis of shared clinical trial data

Overview of potential biases and sources of multiplicity ICH (International Council for Harmonisation of Tech- nical Requirements for Pharmaceuticals for Human Use) E9 ‘Statistical Principles for Clinical Trials’ is a key guid- ance for statistical methodology applied to clinical trials for marketing applications demonstrating the safety and efficacy of medicinal products [12]. Throughout the ICH E9 guidance there is emphasis on the use of pre- specification to protect against multiplicity of methods and interpretation and of blind review (checking of data prior to the breaking of the blind) in order that unbiased decisions about the analysis methods can be made. For confirmatory trials of medicines, key statistical methods will be defined in the protocol prior to initiation of the trial, and a statistical analysis plan will be written prior to un-blinding of the data. The analysis plan will provide de- tails of the analysis populations, the derivation of vari- ables, and the statistical methods, and document any changes from the analyses planned in the protocol. The level of detail contained in a confirmatory trial analysis plan has increased over recent decades, and it is now common for these plans to be from tens to hundreds of pages long. Multiplicity is addressed through the hierarchy of primary, secondary and exploratory objectives. Formal control of the type 1 error may be specified across mul- tiple primary and secondary variables, multiple compari- sons of treatments, and repeated evaluations over time. This framework of pre-planned analyses supports appro- priate interpretation of the results. There will also be an
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