Biblio

In this paper, we propose a zero-distortion blind reversible robust scheme for watermarking relational databases called PKMark. Data owner can declare the copyright of the databases or pursue the infringement by extracting the water-mark information embedded in the database. PKMark is mainly based on the primary key attribute of the tuple. So it does not depend on the type of the attribute, and can provide high-precision numerical attributes. PKMark uses RSA encryption on the watermark before embedding the watermark to ensure the security of the watermark information. Then we use RSA to sign the watermark cipher text so that the owner can verify the ownership of the watermark without disclosing the watermark. The watermark embedding and extraction are based on the hash value of the primary key, so the scheme has blindness and reversibility. In other words, the user can obtain the watermark information or restore the original database without comparing it to the original database. Our scheme also has almost excellent robustness against addition attacks, deletion attacks and alteration attacks. In addition, PKMark is resistant to additive attacks, allowing different users to embed multiple watermarks without interfering with each other, and it can indicate the sequence of watermark embedding so as to indicate the original copyright owner of the database. This watermarking scheme also allows data owners to detect whether the data has been tampered with.

Due to high availability and easy accessibility of information, it has become quite difficult to assure security of data. Even though watermarking seems to be an effective solution to protect data, it is still challenging to be used with relational databases. Moreover, inserting a watermark in database may lead to distortion. As a result, the contents of database can no longer remain useful. Our proposed distortion-free watermarking approach ensures that integrity of database can be preserved by generating an image watermark from its contents. This image is registered with Certification Authority (CA) before the database is distributed for use. In case, the owner suspects any kind of tampering in the database, an image watermark is generated and compared with the registered image watermark. If both do not match, it can be concluded that the integrity of database has been compromised. Experiments are conducted on Forest Cover Type data set to localize tampering to the finest granularity. Results show that our approach can detect all types of attack with 100% accuracy.

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure.

Cryptography is one of the most important sciences today because of the importance of data and the possibility of sharing data via the Internet. Therefore, data must be preserved when stored or transmitted over the Internet. Encryption is used as a solution to protect information during the transmission via an open channel. If the information is obtained illegally, the opponent/ enemy will not be able to understand the information due to encryption. In this paper we have developed a cryptosystem for testing the concepts of multi security level. The information is encrypted using more than one encryption algorithm based on the security level. The proposed cryptosystem concerns of Encryption Based on Multilevel Security (MLS) Model for DBMS. The cryptosystem is designed for both encryption and decryption.

This paper proposes a framework for predicting and mitigating insider collusion threat in relational database systems. The proposed model provides a robust technique for database architect and administrators to predict insider collusion threat when designing database schema or when granting privileges. Moreover, it proposes a real time monitoring technique that monitors the growing knowledgebases of insiders while executing transactions and the possible collusion insider attacks that may be launched based on insiders accesses and inferences. Furthermore, the paper proposes a mitigating technique based on the segregation of duties principle and the discovered collusion insider threat to mitigate the problem. The proposed model was tested to show its usefulness and applicability.