Audit-Free Cloud Storage via Deniable Attribute-based Encryption

                            Audit-Free Cloud Storage via Deniable Attribute-based Encryption

ABSTRACT:

          Cloud storage services have become increasingly popular. Because of the importance of privacy, many cloud storage encryption schemes have been proposed to protect data from those who do not have access. All such schemes assumed that cloud storage providers are safe and cannot be hacked; however, in practice, some authorities (i.e., coercers) may force cloud storage providers toreveal user secrets or confidential data on the cloud, thus altogether circumventing storage encryption schemes. In this paper, we present our design for a new cloud storage encryption scheme that enables cloud storage providers to create convincing fake user secrets to protect user privacy. Since coercers cannot tell if obtained secrets are true or not, the cloud storage providers ensure that user privacy is still securely protected.

EXISTING SYSTEM:

         There are numerous ABE schemes that have been proposed. Most of the proposed schemes assume cloud storage service providers or trusted third parties handling key management are trusted and cannot be hacked; however, in practice, some entities may intercept communications between users and cloud storage providers and then compel storage providers to release user secrets by using government power or other means. In this case, encrypted data are assumed to be known and storage providers are requested to release user secrets. 

         Sahai and Waters first introduced the concept of ABE in which data owners can embed how they want to share data in terms of encryption.

        There are two types of ABE, CP-ABE and Key-Policy ABE (KP-ABE)Goyal et al. proposed the first KPABE. They constructed an expressive way to relate any monotonic formula as the policy for user secret keys. Bethencourt et al. proposed the first CP-ABE. This scheme used a tree access structure to express any monotonic formula over attributes as the 

policy in the ciphertext.

DISADVANTAGES OF EXISTING SYSTEM:

• It is also impractical to encrypt data many times for many people. With ABE, data owners decide only which kind of users can access their encrypted data. Users who satisfy the conditions are able to decrypt the encrypted data.
• Use translucent sets or simulatable public key systems to implement deniability.
• Most deniable public key schemes are bitwise, which means these schemes can only process one bit a time; therefore, bitwise deniable encryption schemes are inefficient for real use, especially in the cloud storage service case.
• Most of the previous deniable encryption schemes are inter-encryption independent. That is, the encryption parameters should be totally different for each encryption operation. If two deniable encryptions are performed in the same environment, the latter encryption will lose deniability after the first encryption is coerced, because each coercion will reduce flexibility.
• Most deniable encryption schemes have decryption error problems. These errors come from the designed decryption mechanisms.

PROPOSED SYSTEM:

           In this work, we describe a deniable ABE scheme for cloud storage services. We make use of ABE characteristics for securing stored data with a fine-grained access control mechanism and deniable encryption to prevent outside auditing. Our scheme is based on Waters ciphertext policy-attribute based encryption (CP-ABE) scheme. We enhance the Waters scheme from prime order bilinear groups to composite order bilinear groups. By the subgroup decision problem assumption, our scheme enables users to be able to provide fake secrets that seem legitimate to outside coercers.

          In this work, we construct a deniable CP-ABE scheme that can make cloud storage services secure and auditfree. In this scenario, cloud storage service providers are just regarded as receivers in other deniable schemes.

ADVANTAGES OF PROPOSED SYSTEM:

• Unlike most previous deniable encryption schemes, we do not use translucent sets or simulatable public key systems to implement deniability. Instead, we adopt the idea proposed with some improvements. We construct our deniable encryption scheme through a multidimensional space. All data are encrypted into the multidimensional space. 
• Only with the correct composition of dimensions is the original data obtainable. With false composition, ciphertexts will be decrypted to predetermined fake data. The information defining the dimensions is kept secret. We make use of composite order bilinear groups to construct the multidimensional space. We also use chameleon hash functions to make both true and fake messages convincing.
• In this work, we build a consistent environment for our deniable encryption scheme. By consistent environment, we means that one encryption environment can be used for multiple encryption times without system updates. The opened receiver proof should look convincing for all ciphertexts under this environment, regardless of whether a cipher text is normally encrypted or deniably encrypted. The deniability of our scheme comes from the secret of the subgroup assignment, which is determined only once in the system setup phase. By the canceling property and the proper subgroup assignment, we can construct the released fake key to decrypt normal ciphertexts correctl

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System  : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
•  Mouse : Logitech.
• Ram : 512 Mb.

SOFTWARE REQUIREMENTS:

• Operating system : Windows XP/7.
• Coding Language : JAVA/J2EE
• IDE : Netbeans 7.4
• Database : MYSQL

REFERENCE:

Po-Wen Chi and Chin-Laung Lei, Member, IEEE, “Audit-Free Cloud Storage via Deniable Attribute-based Encryption”, IEEE Transactions on Cloud Computing, 2015.

Address:

1croreprojects,

Door No: 214/215,2nd Floor,

No. 172, Raahat Plaza, (Shopping Mall)

Arcot Road, Vadapalani, Chennai,

Tamin Nadu, INDIA - 600 026

Website: www.freeinternshipinchennai.co.in 

Phone : +91 77081 50152 / +91 97518 00789 / +91 72999 51536

A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud

                      A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud

ABSTRACT:1croreprojects.com

          Benefited from cloud computing, users can achieve an effective and economical approach for data sharing among group members in the cloud with the characters of low maintenance and little management cost. Meanwhile, we must provide security guarantees for the sharing data files since they are outsourced. Unfortunately, because of the frequent change of the membership, sharing data while providing privacy-preserving is still a challenging issue, especially for an untrusted cloud due to the collusion attack. Moreover, for existing schemes, the security of key distribution is based on the secure communication channel, however, to have such channel is a strong assumption and is difficult for practice. n this paper, we propose a secure data sharing scheme for dynamic members. Firstly, we propose a secure way for key distribution without any secure communication channels, and the users can securely obtain their private keys from group manager. Secondly, our scheme can achieve fine-grained access control, any user in the group can use the source in the cloud and revoked users cannot access the cloud again after they are revoked. Thirdly, we can protect the scheme from collusion attack, which means that revoked users cannot get the original data file even if they conspire with the untrusted cloud.   In our approach, by leveraging polynomial function, we can achieve a secure user revocation scheme. Finally, our scheme can achieve fine efficiency, which means previous users need not to update their private keys for the situation either a new user joins in the group or a user is revoked from the group.

EXISTING SYSTEM:

•  Kallahalla et al presented a cryptographic storage system that enables secure data sharing on untrustworthy servers based on the techniques that dividing files into file groups and encrypting each file group with a file-block key.
• Yu et al exploited and combined techniques of key policy attribute-based encryption, proxy re-encryption and lazy re-encryption to achieve fine-grained data access control without disclosing data contents.

DISADVANTAGES OF EXISTING SYSTEM:

• The file-block keys need to be updated and distributed for a user revocation; therefore, the system had a heavy key distribution overhead. 
•  The complexities of user participation and revocation in these schemes are linearly increasing with the number of data owners and the revoked users.
• The single-owner manner may hinder the implementation of applications, where any member in the group can use the cloud service to store and share data files with others.

PROPOSED SYSTEM:

• In this paper, we propose a secure data sharing scheme, which can achieve secure key distribution and data sharing for dynamic group. 
• We provide a secure way for key distribution without any secure communication channels. The users can securely obtain their private keys from group manager without any Certificate Authorities due to the verification for the public key of the user.
•  Our scheme can achieve fine-grained access control, with the help of the group user list, any user in the group can use the source in the cloud and revoked users cannot access the cloud again after they are revoked.
• We propose a secure data sharing scheme which can be protected from collusion attack. The revoked users can not be able to get the original data files once they are revoked even if they conspire with the untrusted cloud. Our scheme can achieve secure user revocation with the help of polynomial function.
• Our scheme is able to support dynamic groups efficiently, when a new user joins in the group or a user is revoked from the group, the private keys of the other users do not need to be recomputed and updated.
• We provide security analysis to prove the security of our scheme.

ADVANTAGES OF PROPOSED SYSTEM:

• The computation cost is irrelevant to the number of revoked users in RBAC scheme. The reason is that no matter how many users are revoked, the operations for members to decrypt the data files almost remain the same. 
• The cost is irrelevant to the number of the revoked users. The reason is that the computation cost of the cloud for file upload in our scheme consists of two verifications for signature, which is irrelevant to the number of the revoked users. The reason for the small computation cost of the cloud in the phase of file upload in RBAC scheme is that the verifications between communication entities are not concerned in this scheme. 
• In our scheme, the users can securely obtain their private keys from group manager Certificate Authorities and secure communication channels. Also, our scheme is able to support dynamic groups efficiently, when a new user joins in the group or a user is revoked from the group, the private keys of the other users do not need to be recomputed and updated.

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

•  System  : Pentium IV 2.4 GHz.
•  Hard Disk    : 40 GB. 
•  Floppy Drive : 1.44 Mb.
•  Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.

SOFTWARE REQUIREMENTS:

• Operating system : Windows XP/7.
• Coding Language : JAVA/J2EE
•  IDE : Netbeans 7.4
• Database : MYSQL

REFERENCE:

Zhongma Zhu, Rui Jiang, “A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud”, IEEE Transactions on Parallel and Distributed Systems, 2015.

Address:

1croreprojects,

Door No: 214/215,2nd Floor,

No. 172, Raahat Plaza, (Shopping Mall)

Arcot Road, Vadapalani, Chennai,

Tamin Nadu, INDIA - 600 026

Website: www.freeinternshipinchennai.co.in 

Phone : +91 77081 50152 / +91 97518 00789 / +91 72999 51536

co-extracting opinion targets and opinions - 1croreprojects

                        co-extracting opinion targets and opinions

INTRODUCTION :

The proliferation of Web applications, users are expressing their opinion and experiences on blogs, discussion boards, reviews, social networking websites etc. this trend has increased the demand of analyzing this online content resulting into increase in the sentiment analysis research .Sentiment analysis is important for companies and users to know what people think about specific topic. Companies can improve their products, based on users opinion. Users can take purchasing decision based on reviews about the product .Sentiment analysis is to recognize whether a given text express positive or negative polarity.

               Phrases are intuitively very effective in incorporating contextual and syntactic information. This paper explores the methods for extracting phrases that are important for sentiment classification.In this paper, initially phrases are extracted using POS based rules and dependency relations. POS based rules are able to extract sentiment-rich phrases which incorporates contextual information and dependency relation based phrases are capable of incorporating syntactic information of document. After extraction of sentiment-rich phrases, semantic orientation of all these phrases is computed using PMI method. Finally, overall semantic orientation of the document is determined by aggregating the semantic orientation of all the phrases.

                   Document-level sentiment analysis mainly makes simplestatistics for orientation values to obtain the whole tendency of texts. It shows high-speed operation and simple to achieve. However, it is just applied to document sentiment classification, such as polarity analysis for news reports or political reviews, since analysis results are too coarse and lack of extraction of sentiment analysis for related attribute.Sentence-level sentiment analysis mainly classifying sentences/clauses as subjective or objective, and classifying subjective sentences/clauses as positive or negative. Many researchers aim to solve the general problem in sentence-level polarity analysis. However they ignore to analyze the items of sentence so that sentence-level analysis has not good performance when the structure of sentence is more complex.

Abstract :

         Sentiment Analysis determines the polarity of text whether it belongs to positive or negative polarity. One motivation for sentiment analysis research is the need for user and e-commercecompanies to know the public opinion from blogs, online forums, reviews about certain products, services, topics etc. Phrases are important in extracting contextual information which is important for sentiment classification. Phrases can convey sentiment information more efficiently than individual words. In this paper, sentiment-rich phrases are extracted using Part-of-speech (POS) based rules and dependency relation in the document that are capable of extracting contextual and syntactic information from the document. Next, semantic orientations of these phrases are calculated using Point-wise Mutual Information (PMI) based method. Finally, review document is classified after aggregating the semantic orientation of all the phrases into positive or negative polar document.

EXISTING SYSTEM:

·         In previous methods, mining the opinion relations between opinion targets and opinion words was the key to collective extraction. To this end, the most adopted techniques have been nearest-neighbor rules and syntactic patterns.

·         Nearest neighbor rules regard the nearest adjective/verb to a noun/noun phrase in a limited window as its modifier.

·         Syntactic information, in which the opinion relations among words are decided according to their dependency relations in the parsing tree.

DISADVANTAGES OF EXISTING SYSTEM:

• Nearest neighbor rules strategy cannot obtain precise results because there exist long-span modified relations and diverse opinion expressions.
• Syntactic patterns are prone to errors. Online reviews usually have informal writing styles, including grammatical errors, typographical errors, and punctuation errors. This makes the existing parsing tools, which are usually trained on formal texts such as news reports, prone to generating errors.
• The collective extraction adopted by most previous methods was usually based on a bootstrapping framework, which has the problem of error propagation

PROPOSED SYSTEM:

·         To precisely mine the opinion relations among words, we propose a method based on a monolingual word alignment model (WAM). An opinion target can find its corresponding modifier through word alignment.

·         We further notice that standard word alignment models are often trained in a completely unsupervised manner, which results in alignment quality that may be unsatisfactory. We certainly can improve alignment quality by using supervision. However, it is both time consuming and impractical to manually label full alignments in sentences. Thus, we further employ a partially-supervised word alignment model (PSWAM).

·         We believe that we can easily obtain a portion of the links of the full alignment in a sentence. These can be used to constrain the alignment model and obtain better alignment results. To obtain partial alignments, we resort to syntactic parsing.

·         To alleviate the problem of error propagation, we resort to graph co-ranking. Extracting opinion targets/ words is regarded as a co-ranking process. Specifically, a graph, named as Opinion Relation Graph, is constructed to model all opinion target/word candidates and the opinion relations among them.

ADVANTAGES OF PROPOSED SYSTEM:

• ·Compared to previous nearest-neighbor rules, the WAM does not constrain identifying modified relations to a limited window; therefore, it can capture more complex relations, such as long-span modified relations.
• ·Compared to syntactic patterns, the WAM is more robust because it does not need to parse informal texts. In addition, the WAM can integrate several intuitive factors, such as word co-occurrence frequencies and word positions, into a unified model for indicating the opinion relations among words. Thus, we expect to obtain more precise results on opinion relation identification.
• ·  The alignment model used has proved to be effective for opinion target extraction.

 Conclusion :

This paper proposes a novel method for co-extracting opinion targets and opinion words by using a word alignment

model. Our main contribution is focused on detecting opinion relations between opinion targets and opinion words.

Compared to previous methods based on nearest neighbour rules and syntactic patterns, in using a word alignment

model, our method captures opinion relations more precisely and therefore is more effective for opinion target and

opinion word extraction. Next, we construct an Opinion Relation Graph to model all candidates and the detectedopinion relations among them, along with a graph co-rankingalgorithm to estimate the confidence of each candidate.

The items with higher ranks are extracted out. Theexperimental results for three datasets with different languages

and different sizes prove the effectiveness of the

proposed method.

In future work, we plan to consider additional types of relations between words, such as topical relations, in Opinion

Relation Graph. We believe that this may be beneficial for co-extracting opinion targets and opinion words

References :

[1] M. Hu and B. Liu, “Mining and summarizing customer reviews,”in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Seattle, WA, USA, 2004, pp. 168–177.

[2] F. Li, S. J. Pan, O. Jin, Q. Yang, and X. Zhu, “Cross-domain coextractionof sentiment and topic lexicons,” in Proc. 50th Annu.

Meeting Assoc. Comput. Linguistics, Jeju, Korea, 2012, pp. 410–419.

[3] L. Zhang, B. Liu, S. H. Lim, and E. O’Brien-Strain, “Extracting and ranking product features in opinion documents,” in Proc. 23th Int.

Conf. Comput. Linguistics, Beijing, China, 2010, pp. 1462–1470.

[4] K. Liu, L. Xu, and J. Zhao, “Opinion target extraction using wordbasedtranslation model,” in Proc. Joint Conf. Empirical Methods

Natural Lang. Process.Comput. Natural Lang. Learn., Jeju, Korea, Jul. 2012, pp. 1346–1356.

[5] M. Hu and B. Liu, “Mining opinion features in customer reviews,”in Proc. 19th Nat. Conf. Artif.Intell., San Jose, CA, USA, 2004,

pp. 755–760.

[6] A.-M. Popescu and O. Etzioni, “Extracting product features and opinions from reviews,” in Proc. Conf. Human Lang. Technol. EmpiricalMethods Natural Lang. Process., Vancouver, BC, Canada, 2005, pp. 339–346.

[7] G. Qiu, L. Bing, J. Bu, and C. Chen, “Opinion word expansion and target extraction through double propagation,” Comput. Linguistics,

vol. 37, no. 1, pp. 9–27, 2011.

[8] B. Wang and H. Wang, “Bootstrapping both product features and opinion words from chinese customer reviews with crossinducing,”

inProc. 3rd Int. Joint Conf. Natural Lang. Process., Hyderabad, India, 2008, pp. 289–295.

[9] B. Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage

Data, series Data-Centric Systems and Applications. New York,

NY, USA: Springer, 2007.

[10] G. Qiu, B. Liu, J. Bu, and C. Che, “Expanding domain sentiment lexicon through double propagation,” in Proc. 21st Int. Jont Conf.

Secure Auditing and Deduplicating Data in Cloud - 1croreprojects

SecureAuditing and Deduplicating Data in Cloud

ABSTRACT:

As the cloud computing technology develops during the last decade, outsourcing data to cloud service for storage becomes an attractive trend, which benefits in sparing efforts on heavy data maintenance and management. Nevertheless, since the outsourced cloud storage is not fully trustworthy, it raises security concerns on how to realize data deduplication in cloud while achieving integrity auditing. In this work, we study the problem of integrity auditing and secure deduplication on cloud data. Specifically, aiming at achieving both data integrity and deduplication in cloud, we propose two secure systems, namely SecCloud and SecCloud+. SecCloud introduces an auditing entity with a maintenance of a MapReduce cloud, which helps clients generate data tags before uploading as well as audit the integrity of data having been stored in cloud. Compared with previous work, the computation by user in SecCloud is greatly reduced during the file uploading and auditing phases. SecCloud+ is designed motivated by the fact that customers always want to encrypt their data before uploading, and enables integrity auditing and secure deduplication on encrypted data.

EXISTING SYSTEM:

• Ateniese et al. proposed a dynamic PDP schema but without insertion operation.
• Erway et al. improved Ateniese et al.’s work and supported insertion by introducing authenticated flip table.
• Wang et al.proposed proxy PDP in public clouds.
• Zhu et al. proposed the cooperative PDP in multi-cloud storage.
• Wang et al. improved the POR model by manipulating the classic Merkle hash tree construction for block tag authentication.
• Xu and Chang proposed to improve the POR schema with polynomial commitment for reducing communication cost.
• Stefanov et al. proposed a POR protocol over authenticated file system subject to frequent changes.
• Azraoui et al. combined the privacy-preserving word search algorithm with the insertion in data segments of randomly generated short bit sequences, and developed a new POR protocol.
• Li et al. considered a new cloud storage architecture with two independent cloud servers for integrity auditing to reduce the computation load at client side.

DISADVANTAGES OF EXISTING SYSTEM:

• The first problem is integrity auditing. The cloud server is able to relieve clients from the heavy burden of storage management and maintenance. The most difference of cloud storage from traditional in-house storage is that the data is transferred via Internet and stored in an uncertain domain, not under control of the clients at all, which inevitably raises clients great concerns on the integrity of their data.
• The second problem is secure deduplication. The rapid adoption of cloud services is accompanied by increasing volumes of data stored at remote cloud servers. Among these remote stored files, most of them are duplicated: according to a recent survey by EMC, 75% of recent digital data is duplicated copies.
• Unfortunately, this action of deduplication would lead to a number of threats potentially affecting the storage system, for example, a server telling a client that it (i.e., the client) does not need to send the file reveals that some other client has the exact same file, which could be sensitive sometimes. These attacks originate from the reason that the proof that the client owns a given file (or block of data) is solely based on a static, short value (in most cases the hash of the file).

PROPOSED SYSTEM:

• In this paper, aiming at achieving data integrity and deduplication in cloud, we propose two secure systems namely SecCloud and SecCloud+.
• SecCloud introduces an auditing entity with maintenance of a MapReduce cloud, which helps clients generate data tags before uploading as well as audit the integrity of data having been stored in cloud.
• Besides supporting integrity auditing and secure deduplication, SecCloud+ enables the guarantee of file confidentiality.
• We propose a method of directly auditing integrity on encrypted data.

ADVANTAGES OF PROPOSED SYSTEM:

• This design fixes the issue of previous work that the computational load at user or auditor is too huge for tag generation. For completeness of fine-grained, the functionality of auditing designed in SecCoud is supported on both block level and sector level. In addition, SecCoud also enables secure deduplication.
• The challenge of deduplication on encrypted is the prevention of dictionary attack.
• Our proposed SecCloud system has achieved both integrity auditing and file deduplication.

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Floppy Drive : 1.44 Mb.

• Monitor : 15 VGA Colour.

• Mouse : Logitech.

• Ram : 512 Mb.

SOFTWARE REQUIREMENTS:

• Operating system : Windows XP/7.
• Coding Language : JAVA/J2EE
• IDE : Netbeans 7.4
• Database : MYSQL

REFERENCE:

Jingwei Li, Jin Li, Dongqing Xie and Zhang Cai, “Secure Auditing and Deduplicating Data in Cloud”, IEEE Transactions on Computers 2015.