Data provenance with retention of reference relations

With the development of data transactions, data security issues have become increasingly important. For example, the copyright authentication and provenance of data have become the primary requirements for data security defence mechanisms. For this purpose, this paper proposes a data provenance system with retention of reference relations (called RRDP), which can enhance the security of data service in the process of publishing and transmission. The system model for data provenance with retention of reference relations adds virtual primary keys using reference relations between data tables. Traditional provenance algorithms have limitations on data types. This model has no such limitations. Added primary key is auto-incrementing integer number. Multi-level encryption is performed on the data watermarking to ensure the secure distribution of data. The experimental results show that the data provenance system with retention of reference relations has good accuracy and robustness of the provenance about common database attacks

Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

Contrary to multimedia data watermarking approaches, it is not recommended that relational data watermarking techniques consider sequential selection for marks in the watermark and embedding locations in the protected digital asset. Indeed, considering the database relations' elements, i.e., tuples and attributes, when watermarking techniques are based on sequential processes, watermark detection can be easily compromised by performing subset reverse order attacks. As a result, attackers can obtain owner evidence-free high-quality data since no data modifications for mark removing are required for the malicious operation to succeed. A standard solution to this problem has been pseudo-random selection, which often leads to choosing the same marks multiple times, and ignoring others, thus compromising the embedding of the entire watermark. This work proposes an engine that contributes to controlling marks' recurrent selection, allowing marks excluded by previous approaches to be considered and detected with 100% accuracy. The experiments performed show a dramatic improvement of the embedded watermark quality when the proposed engine is included in watermarking techniques' architecture. They also provide evidence that this proposal leads to higher resilience against common malicious operations such as subset and superset attacks

Assessing the vulnerabilities and securing MongoDB and Cassandra databases

Due to the increasing amounts and the different kinds of data that need to be stored in the databases, companies, and organizations are rapidly adopting NoSQL databases to compete. These databases were not designed with security as a priority. NoSQL open-source software was primarily developed to handle unstructured data for the purpose of business intelligence and decision support. Over the years, security features have been added to these databases but they are not as robust as they should be, and there is a scope for improvement as the sophistication of the hackers has been increasing. Moreover, the schema-less design of these databases makes it more difficult to implement traditional RDBMS like security features in these databases. Two popular NoSQL databases are MongoDB and Apache Cassandra. Although there is a lot of research related to security vulnerabilities and suggestions to improve the security of NoSQL databases, this research focusses specifically on MongoDB and Cassandra databases. This study aims to identify and analyze all the security vulnerabilities that MongoDB and Cassandra databases have that are specific to them and come up with a step-by-step guide that can help organizations to secure their data stored in these databases. This is very important because the design and vulnerabilities of each NoSQL database are different from one another and hence require security recommendations that are specific to them

Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks

The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains that can build on a solid understanding of the threats, attacks and defenses available to protect their IP, the ML-related research in this regard is still very fragmented. This is also due to a missing unified view as well as a common taxonomy of these aspects. In this paper, we systematize our findings on IPP in ML, while focusing on threats and attacks identified and defenses proposed at the time of writing. We develop a comprehensive threat model for IP in ML, categorizing attacks and defenses within a unified and consolidated taxonomy, thus bridging research from both the ML and security communities

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table