Abstract Massive databases encounter security such as data theft, illegal copying, and copyright infringement during creating, transmitting, and sharing of big data. The reversible data watermarking technology can effectively solve these problems, which can extract the watermark information accurately and recover the original carrier data without any distortion. However, most existing methods extract watermark information non-blindly and cannot effectively achieve a balance between watermark embedding capacity and data distortion. This paper proposes a blind reversible database watermarking method based on dual embedding, which combines histogram shifting and distortion-free watermarking methods to achieve an adaptive selection of histogram bins, blind extraction of watermark information, and carrier data recovery. The proposed method preprocesses the database tuples by scrambling them and constructs a prediction error histogram using first-layer tuples in square prediction within each group. The watermark information is embedded through adaptive selection and expansion of histogram bins, while the distortion-free watermarking method is used in another layer to assist in the recovery of original carrier data. The experimental results show that the proposed method can achieve an embedding capacity of more than three times the capacity of existing methods. It can also achieve blind watermark extraction and outperform some other state-of-the-art methods.

Database watermarking is typically employed to address the issues of data theft, illegal replication, and copyright infringement that may arise during the sharing of databases. Unfortunately, the existing methods often cause permanent distortion to the original data, and it is challenging to strike a balance between the watermark embedding capacity and data distortion. Therefore, this paper proposes a reversible database watermarking method based on histogram shifting, rhombus prediction, and double embedding with high capacity and low distortion, called RPDE-HSW. By utilizing the rhombus prediction, we respectively constructed two prediction error histograms in each subgroup and expanded the watermark capacity through the adoption of double-layer embedding and single-bin embedding 2 bits. A scrambling algorithm is used to make the attribute value distribution more discretized, resulting in a sparse distribution of the database histogram. Subsequently, we optimized the selection rules for the watermark embedding carrier, effectively eliminating the redundant distortion caused by histogram shifting. Experimental results demonstrate that the proposed method achieves smaller data distortion and higher watermark embedding capacity, outperforming some other state-of-the-art works, and does not affect the classification results and data mining.

In this poster, we proposed a .NET patch analysis framework named MSILDiffer based on Microsoft Intermediate Language (MSIL). First, MSILDiffer directly extracts MSIL instructions from the .NET assemblies, and retrieves the hierarchy of classes as well as their internal class methods. Then, with coarse and fine granularity feature extraction and comparison, MSILDiffer quickly filters out the code with substantial changes after patch. Besides, we build a dataset of patch analysis containing 24.46 million class methods based on the Microsoft Exchange mail system security patches. With the assistance of MSILDiffer, we generated 32 call paths and crafted corresponding POCs for 1-day vulnerabilities in the dataset. Through the experiment evaluation, MSILDiffer is superior to JustAssembly in terms of coverage, accuracy and time consumption of patch difference analysis.