Rights protection of trajectory datasets with nearest-neighbor preservation

Abstract

Companies frequently outsource datasets to mining firms, and academic institutions create repositories or share datasets in the interest of promoting research collaboration. Still, many practitioners have reservations about sharing or outsourcing datasets, primarily because of fear of losing the principal rights over the dataset. This work presents a way of convincingly claiming ownership rights over a trajectory dataset, without, at the same time, destroying the salient dataset characteristics, which are important for accurate search operations and data-mining tasks. The digital watermarking methodology that we present distorts imperceptibly a collection of sequences, effectively embedding a secret key, while retaining as well as possible the neighborhood of each object, which is vital for operations such as similarity search, classification, or clustering. A key contribution in this methodology is a technique for discovering the maximum distortion that still maintains such desirable properties. We demonstrate both analytically and empirically that the proposed dataset marking techniques can withstand a number of attacks (such a translation, rotation, noise addition, etc) and therefore can provide a robust framework for facilitating the secure dissemination of trajectory datasets. © 2010 Springer-Verlag

On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques

“…2) Lucchese et al [70] proposed a novel watermarking scheme that preserves Nearest-Neighbors (NNs) of objects in a trajectory dataset. In other words, the k nearest neighbors of each object remains the same after watermark insertion and therefore the usability of dataset is preserved under NN-search and NN-classification.…”

“…Watermarking of trajectories is a challenging problem and depends on the characteristics of objects in the dataset such as variance. For instance, a busy trajectory gives the opportunity to store more information whereas a very smooth line limits the watermark capacity [70]. The watermarking methods that we review in this section have particular interest in retaining the usability of dataset for mining algorithms.…”

Advancements in GIS map copyright protection schemes - a critical review

“…The main advantage of FT is its invariance property against some geometric attacks like translation, scaling and rotation [56,91]. CT is another digital transform that separate the vector map into parts of different frequency with respect to the vector map visual quality.…”

Right-protected data publishing with hierarchical clustering preservation

“…Finally, we assess the resilience of our scheme against a broad range of potential attacks: geometric distortions, resampling, etc. We test our methods on datasets from various application areas: mobility data (taxi trajectories in Beijing [32,33] and San Francisco [21]), financial data (stock prices in the NASDAQ stock market), video-tracking data, handwritten data, and image contour data from anthropology and natural sciences (the latter datasets were obtained from [15] …”

“…A rightprotection scheme based on watermarking principles that preserved the Nearest Neighbor of objects was presented in [15]. We adopt the watermarking model of that work, but here we study the more elaborate case of hierarchical clustering preservation.…”