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.

Mining of data is used to analyze facts to discover formerly unknown patterns, classifying and grouping the records. There are several crucial scalable statistics mining platforms that have been developed in latest years. RapidMiner is a famous open source software which can be used for advanced analytics, Weka and Orange are important tools of machine learning for classifying patterns with techniques of clustering and regression, whilst Knime is often used for facts preprocessing like information extraction, transformation and loading. This article encapsulates the most important and robust platforms.

Covert operations involving clandestine dealings and communication through cryptic and hidden messages have existed since time immemorial. While these do have a negative connotation, they have had their fair share of use in situations and applications beneficial to society in general. A "Dead Drop" is one such method of espionage trade craft used to physically exchange items or information between two individuals using a secret rendezvous point. With a "Dead Drop", to maintain operational security, the exchange itself is asynchronous. Information hiding in the slack space is one modern technique that has been used extensively. Slack space is the unused space within the last block allocated to a stored file. However, hiding in slack space operates under significant constraints with little resilience and fault tolerance. In this paper, we propose FROST – a novel asynchronous "Digital Dead Drop" robust to detection and data loss with tunable fault tolerance. Fault tolerance is a critical attribute of a secure and robust system design. Through extensive validation of FROST prototype implementation on Ubuntu Linux, we confirm the performance and robustness of the proposed digital dead drop to detection and data loss. We verify the recoverability of the secret message under various operating conditions ranging from block corruption and drive de-fragmentation to growing existing files on the target drive.