Free 2018 IEEE (CSE) synopsis of students requested papers

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Secure Data Group Sharing and Dissemination with Attribute and Time Conditions in Public Cloud

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 Paper Synopsis: Data dissemination: Dissemination takes on the theory of the traditional view of communication, which involves a sender and receiver. The traditional communication view point is broken down into a sender sending information, and receiver collecting the information processing it and sending information back, like a telephone line. Example: Alice (owner, share)      รจ       Bob (view, share(with policies)) ( disseminator , re-encrypting the ciphertext) Existing system: Attribute-based encryption Attribute-based encryption (ABE) is one of new cryptographic mechanisms used in cloud to reach flexible and fine-grained secure data group sharing [4]. Especially, ciphertext-policy ABE (CP-ABE) allows data owners to encrypt data with an access policy such that only users whose attributes satisfy the access policy can decrypt the data [5]. The proxy re-encryption (PRE) scheme [7] in a manner could achieve effi...
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Lightweight Fine-Grained Search over Encrypted Data in Fog Computing

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Aim: propose a Lightweight Fine-Grained Search (LFGS) system which supports fine Grained access control and single keyword search. Existing System: ·          the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. ·         existing CP-ABKS schemes are approximately proportional to the complexity of access policy, which greatly hinder the uses of resource-limited IoT devices. ·          existing CP-ABE schemes is that encryption or decryption operations involve time-consuming pairing operations and a number of other operations increasing with the complexity of access policy . Proposed System: Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (...
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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

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Aim: To automatically identify SNMD patients at the early stage according to their OSN data with a novel tensor model that efficiently integrate heterogeneous data from different OSNs. Existing System: The SNMD data from different OSNs may be incomplete due to the heterogeneity (drawback) . For example, the profiles of users may be empty due to the privacy issue, different functions on different OSNs (e.g., game, check-in, event), etc.  Proposed System : propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. The paper also exploits multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy.   Advantage: propose a novel tensor-based approach to address the issues of using heterogeneous data and incorporate domain knowledge in SNMD detection. Objectives: ·   ...
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