An Ownership Verification Mechanism Against Encrypted Forwarding Attacks in Data-Driven Social Computing

Data-driven deep learning has accelerated the spread of social computing applications. To develop a reliable social application, service providers need massive data on human behavior and interactions. As the data is highly relevant to users’ privacy, researchers have conducted extensive research on how to securely build a collaborative training model. Cryptography methods are an essential component of collaborative training which is used to protect privacy information in gradients. However, the encrypted gradient is semantically invisible, so it is difficult to detect malicious participants forwarding other’s gradient to profit unfairly. In this paper, we propose a data ownership verification mechanism based on Σ-protocol and Pedersen commitment, which can help prevent gradient stealing behavior. We deploy the Paillier algorithm on the encoded gradient to protect privacy information in collaborative training. In addition, we design a united commitment scheme to complete the verification process of commitments in batches, and reduce verification consumption for aggregators in large-scale social computing. The evaluation of the experiments demonstrates the effectiveness and efficiency of our proposed mechanism.

Text Data Security and Privacy in the Internet of Things: Threats, Challenges, and Future Directions

In our daily life, Internet-of-Things (IoT) is everywhere and used in many more beneficial functionalities. It is used in our homes, hospitals, fire prevention, and reporting and controlling the environmental changes. Data security is the crucial requirement for IoT since the number of recent technologies in different domains is increasing day by day. Various attempts have been made to cater the user’s demands for more security and privacy. However, a huge risk of security and privacy issues can arise among all those benefits. Digital document security and copyright protection are also important issues in IoT because they are distributed, reproduced, and disclosed with extensive use of communication technologies. The content of books, research papers, newspapers, legal documents, and web pages are based on plain text, and the ownership verification and authentication of such documents are essential. In the current domain of the Internet of Things, limited techniques are available for ownership verification and copyright protection. In the said perspective, this study includes the discussion about the approaches of text watermarking, IoT security challenges, IoT device limitations, and future research directions in the area of text watermarking.

Digital Video Watermarking Using Diverse Watermarking Schemes

As the significance of the internet is increasing day by day so is the need of protecting the media over the internet. In order to protect the copyright information of the media over the internet, the authors use the technique of watermarking. Watermarking is the process of embedding a watermark in the media and then extracting it for ownership verification. Different types of watermarking schemes exist in the world, but we always look for techniques which are highly imperceptible and do not lead to loss of fidelity. Here the researchers have put forward a technique that instills different watermarking schemes to different sets of frames.

Digital Video Watermarking Using Diverse Watermarking Schemes

As the significance of the internet is increasing day by day so is the need of protecting the media over the internet. So in order to protect the copyright information of the media over the internet we use the technique of Watermarking. Watermarking is the process of embedding a watermark in the media and then extracting it for ownership verification. Different types of watermarking schemes exist in the world but we always look for techniques which are highly imperceptible and does not lead to loss of fidelity. Here the researchers have put forward a technique which instills different watermarking schemes to different set of frames.