Lime: Data Lineage in the Malicious Environment

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol

A Systematic Review on Image Data Protection Methods

Securing data is the main goal of any data security system (DSS). Valuable data must be protected all the time and stored in a very highly secure data storage device. This need has become more critical due to the continuous growth of data size.  Furthermore, non-text data in the form of images, audio, and videos can now be transferred and processed easily and thus become part of sensitive data that needs to be protected. Since there is a need to secure and protect data in any form in order to keep them private and valid, it is expected that there would be many attempts already that have been proposed in the literature for this purpose. This paper reviews a group of these proposed strategies and methods that have been applied to different kinds of DSSs. Challenges and future trends of DSSs are also discussed. A number of main findings are grouped and organized as follows: (1) there are many different kinds of security techniques, each of which offers varying degrees of performance in terms of the amount of data and information that can be managed securely, (2) depending on the architecture of the proposed method, the tactics or strategies of the security system, the kinds of DSSs, as well as a few other factors, some methods are more appropriate for the storage of certain categories of data than others

Exploring adversarial attacks in federated learning for medical imaging

Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities of federated learning networks in medical image analysis against such attacks. Employing domain-specific MRI tumor and pathology imaging datasets, we assess the effectiveness of known threat scenarios in a federated learning environment. Our tests reveal that domain-specific configurations can increase the attacker's success rate significantly. The findings emphasize the urgent need for effective defense mechanisms and suggest a critical re-evaluation of current security protocols in federated medical image analysis systems

Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond

Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development. The content generated by related applications, such as text, images and audio, has sparked a heated discussion. Various derived AIGC applications are also gradually entering all walks of life, bringing unimaginable impact to people's daily lives. However, the rapid development of such generative tools has also raised concerns about privacy and security issues, and even copyright issues in AIGC. We note that advanced technologies such as blockchain and privacy computing can be combined with AIGC tools, but no work has yet been done to investigate their relevance and prospect in a systematic and detailed way. Therefore it is necessary to investigate how they can be used to protect the privacy and security of data in AIGC by fully exploring the aforementioned technologies. In this paper, we first systematically review the concept, classification and underlying technologies of AIGC. Then, we discuss the privacy and security challenges faced by AIGC from multiple perspectives and purposefully list the countermeasures that currently exist. We hope our survey will help researchers and industry to build a more secure and robust AIGC system.Comment: 43 pages, 10 figure

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture

Roadmap on optical security

Postprint (author's final draft

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology