Sharing data via social media may affect the privacy of other user's in social media. Also, multiparty privacy management is absent in social media, which leads the users incapable of managing to whom the data are...
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Sharing data via social media may affect the privacy of other user's in social media. Also, multiparty privacy management is absent in social media, which leads the users incapable of managing to whom the data are shared. Because of the privacy conflicts, it is not easy to combine the privacy preferences of multiple users. For resolving the privacy conflicts in social media, more methods are required. This study promotes a fuzzy-based multiparty privacy management in social media using modified elliptic curve cryptography. The evaluation model used a method based on secure multiparty computing. Next, the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) method is used to rank and select the participants. Finally, data encryption is performed using a modifiedellipticcurve encryption (MECC). Here, the optimal selection of private key is performed using the cuckoo search optimization algorithm (CSOA). With these presented techniques, the users can manage who the data are shared. In order to overcome privacy conflicts, users may first rank and select the participants based on fuzzy TOPSIS. Also, the privacy of the users is not affected by using the MECC-based data encryption framework. The presented work is implemented on the JAVA platform. The outcomes of the experiment prove that the presented approach outperforms the other existing approaches.
The Internet of Things (IoT) consists of a massive count of connected devices with different sensing support, particularly in medical health constraints sensing. In these summaries, secure communication and data colle...
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The Internet of Things (IoT) consists of a massive count of connected devices with different sensing support, particularly in medical health constraints sensing. In these summaries, secure communication and data collection to centralized servers is rather difficult for preventing the occurrence of diverse attacks for illegal data access. To tackle these problems, this task plans to design and implement the FoG-based secure data management system in IoT-WSN using adaptive encryption and decryption processes. As the FoG server is away from the corresponding aggregator node, it is transmitted via the other nodes using the proposed modified elliptic curve cryptography (ECC)-based encryption process. For secured data transmission the designed method undergoes two main stages as message receiving stage, and the message-extraction stage. The hybridized meta-heuristic algorithm termed Jaya-Galactic Swarm Optimization (J-GSO) is used for modifying the ECC with an optimized key. From the simulation findings, the performance of the suggested scheme at the data size of three bytes is 3.08%, 3.22%, 3.65%, and 3.33% better than the MFO-m-ECC, PSO-m-ECC, GSO-m-ECC, and JA-m-ECC at the ECC curve variation of "secp192r1". Experimental results reveal the superiority of the designed method when tested with baseline schemes regarding time complexity, space complexity, and cost function.
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