All posts by SmartBit Infotech

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Software company in Pune, Maharashtra

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Introduction :

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud. Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. Firstly, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Secondly, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext.

Reference IEEE paper:

“Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud”, IEEE Transactions on Big Data, 2017.

Unique ID – SBI1082


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Topic Rehotting Prediction in Online Social Networks

Topic rehotting prediction is popular technique in social networks. It is really popular to detect hot topics, which can benefit many tasks including topic recommendations, the guidance of public opinions, and so on. However, in some cases, people may want to know when to re-hot a topic, i.e., make the topic popular again. In this paper, we address this issue by introducing a temporal User Topic Participation (UTP) model which models users behaviours of posting messages. The UTP model takes into account users interests, friend-circles, and unexpected events in online social networks. Also, it considers the continuous temporal modelling of topics, since topics are changing continuously over time. Furthermore, a weighting scheme is proposed to smooth the fluctuations in topic re-hotting prediction. Finally, experimental results conducted on real world data sets demonstrate the effectiveness of our proposed models and topic re-hotting prediction methods.

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