Yuwei Peng

Watermarking geographical data on spatial topological relations

Geographical data is of great value for data producers. How to protect copyright of geographical data effectively using digital watermarking is a hot research issue. In this paper, spatial topological relation existing between polygons is chosen as cover data, and watermark is embedded by slightly modifying the metric measure of spatial topological relation, and some geographical objects are scaled to derive the watermarked data. Global Main Scaling Distance is introduced to measure the distortions caused by watermark embedding. Experimental results show that the proposed algorithm has a good robustness against geometrical attacks, simplification, interpolation and noise addition attacks, and preserves shape of the watermarked geographical objects. Furthermore, a good tradeoff between robustness and fidelity is acquired in the proposed algorithm.

Blind watermarking scheme for polylines in vector geo-spatial data

With the rapid development of GIS and computer technology, the geo-spatial data has been widely used in many fields. Since the production of geo-spatial data is very costly, illegal copying will lead to huge loss of the data owner. In order to protect the copyright of geo-spatial data, digital watermarking has been employed in recent years. In this paper, current works on geo-spatial watermarking and cardinal attacks are analyzed. Consequently, a blind watermarking scheme for polylines in vector geo-spatial data is proposed. In the proposed scheme, FVDR (feature vertex distance ratio) is constructed based on the feature vertices of polylines. As the cover data, FVDR is slightly modified through quantization index modulation to embed the corresponding watermark bit. Each bit in the watermark is embedded in several polylines, by doing so each of these polylines contains a copy of the corresponding watermark bit. Because of the specially designed cover data, quantization modulation and redundant embedding, the proposed scheme is robust to geometrical attacks, vertex attacks and object attacks. Also, the results of extensive experiments demonstrate the robustness of the proposed scheme.

HTPR*-Tree: an efficient index for moving objects to support predictive query and partial history query

Developing efficient index structures is an important issue for moving object database. Currently, most indexing methods of moving objects are focused on the past position, or on the present and future one. In this paper, we propose a novel indexing method, called HTPR*-tree (History Time-Parameterized R-tree), which not only supports predictive queries but also partial history ones involved from the most recent update instant of each object to the last update time. Based on the TPR*-tree, our HTPR*-tree adds creation or update time of moving objects to leaf node entries. This index is the foundation of indexing the past, present and future positions of moving objects. In order to improve the update performance, we present a bottom-up update strategy for the HTPR*-tree by supplementing three auxiliary structures which include hash index, bit vector, and direct access table. Experimental results show that the update performance of the HTPR*-tree is better than that of the TD_HTPR*- and TPR*-tree. Moreover, the HTPR*-tree can support partial history queries compared with TPR*-tree although the predictive query performance is a bit less.

Indexing the Past, Present and Future Positions of Moving Objects on Fixed Networks

The development of a spatio-temporal access method suitable for objects moving on fixed networks is a very attractive challenge due to a large number of applications dealing with this type of objects. In this paper, we propose a novel indexing technique named PPFI which consists of a 2D R*-tree, a forest of 1D R*-tree, and a hash structure. PPFI not only supports queries related to the past positions or trajectories of moving objects on fixed networks, but also provides an efficient update mechanism, stores current positions and supports predictive query. The performance study, comparing this access method with FNR-Tree, STR-Tree under range query and trajectory query, shows that PPFI outperforms them.

Multipurpose watermarking for vector map protection and authentication

The rapid flourishing of GIS applications has made the copyright protection and content authentication of vector maps two important issues in the digital world. In this paper, a multipurpose watermarking scheme is proposed for the scenarios when the two requirements are needed simultaneously. In the scheme, robust watermark and fragile watermarks are embedded into the host map simultaneously. To avoid the interference between the two kinds of watermarks, robust watermark is embedded into the feature points of the objects in the map, while fragile watermarks are embedded into the nonfeature points. Due to the independence of the feature points and non-feature points, the robust watermark and fragile watermarks can be detected independently. After watermark detection, the robust watermark can be used for copyright protection, and the fragile watermarks can be used for tamper localization and characterization. The fragility of the proposed scheme is analyzed theoretically, and both the robustness and the fragility are verified by a set comprehensive experiments.

Anonymous Watermarking Protocol for Vector Spatial Data

In this paper, with current cryptography theory and watermarking technology of vector spatial data, we design a watermarking protocol for vector spatial data. This protocol combines trusted third party protocol and buyer-seller protocol. In this protocol, digital signature technique is employed to identity buyer and seller involved in the trade of spatial data product. Trusted third party transforms watermark information used in the trade to ensure the secrecy. Anonymous mechanism is employed in this protocol, the buyer has the anonymity to protect its privacy. In the watermark embedding process, we make the embedding operation and the encrypted operation into an atomic operation, to protect the interests of consumers. In addition, all information is encrypted to prevent disclosure in every stage of the trade. After theoretical analysis, it shows that this protocol can solve the security aspects of watermarking protocol and can resist all kinds of attacks against the protocol.

A New Similarity Measure Between Semantic Trajectories Based on Road Networks

With the development of the positioning technology, studies on trajectories have been growing rapidly in the past decades. As a fundamental part involved in trajectory recommendation and prediction, trajectory similarity has attracted considerable attention from researchers. However, most existing works focus on raw trajectory similarity by comparing their shapes, while very few works study semantic trajectory similarity and none of them take all the geographical, semantic, and timestamp information into consideration. In this paper, we model semantic trajectories based on road networks considering all these information, and propose a Constrained Time-based Common Parts (CTCP) approach to measure the similarity. Since the strict time constraint in CTCP may lead to many zero values, we further propose an improved Weighted Constrained Time-based Common Parts (WCTCP) method by relaxing the time constraint to measure the similarity more accurately. We conducted extensive performance studies on real datasets to confirm the effectiveness of our approaches.

Rights protection for trajectory streams

More and more trajectory data are available as streams due to the unprecedented prevalence of mobile positioning devices. Meanwhile, an increasing number of applications are designed to be dependent on real-time trajectory streams. Therefore, the protection of ownership rights over such data becomes a necessity. In this paper, we propose an online watermarking scheme that can be used for the rights protection of trajectory streams. The scheme works in a finite window, single-pass streaming model. It embeds watermark by modifying feature distances extracted from the streams. The fact that these feature distances can be recovered ensures a consistent overlap between the recovered watermark and the embedded one. Experimental results verify the robustness of the scheme against domain-specific attacks, including geometric transformations, noise addition, trajectory segmentation and compression.

Indexing partial history trajectory and future position of moving objects using HTPR*-Tree

Currently, most indexing methods of moving objects are focused on the past position, or the present and future one. In this paper, we propose a novel indexing method, called History TPR*-tree(HTPR*-tree), which not only supports predictive queries but also partial history ones involved from the most recent update instant of each object to the last update time of all objects. Based on the TPR*-tree, our Basic HTPR*-tree adds creation or update time of moving objects to leaf node entries. In order to improve the update performance, we present a bottom-up update strategy for the HTPR*-tree by supplementing a hash table, a bit vector and a direct access table. Experimental results show that the update performance of the HTPR*-tree is better than that of the Basic HTPR*-and TPR*-tree. In addition to support partial history queries, the update and predictive query performance of the HTPR*-tree are greatly improved compared with those of the RPPF-tree.

A 2D vector map watermarking algorithm resistant to simplication attack

Vector maps are valuable asset of data producers. How to protect copyright of vector maps effectively using digital watermarking is a hot research issue. In this paper, we propose a new robust and blind watermarking algorithm resilient to simplification attack. We proof that spatial topological relation between map objects bears an important property of approximate simplification invariance. We choose spatial topological relations as watermark feature domain and embed watermarks by slightly modifying spatial topological relation between map objects. Experiment shows that our algorithm has good performance to resist simplification attack and tradeoff of the robustness and data fidelity is acquired.