David Gross-Amblard

Query-preserving watermarking of relational databases and Xml documents

Watermarking allows robust and unobtrusive insertion of information in a digital document. During the last few years, techniques have been proposed for watermarking relational databases or Xml documents, where information insertion must preserve a specific measure on data (for example the mean and variance of numerical attributes). In this article we investigate the problem of watermarking databases or Xml while preserving a set of parametric queries in a specified language, up to an acceptable distortion. We first show that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or treelike structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking.

Query-preserving watermarking of relational databases and XML documents

Watermarking allows robust and unobtrusive insertion of information in a digital document. Very recently, techniques have been proposed for watermarking relational databases or XML documents, where information insertion must preserve a specific measure on data (e.g. mean and variance of numerical attributes.)In this paper we investigate the problem of watermarking databases or XML while preserving a set of parametric queries in a specified language, up to an acceptable distortion.We first observe that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or tree-like structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking.

Watermill: An Optimized Fingerprinting System for Databases under Constraints

This paper presents a walermarking/fingerprinting system for relational databases. It features a built-in declarative language to specify usability constraints that watermarked data sets must comply with. For a subset of these constraints, namely, weight-independent constraints, we propose a novel watermarking strategy that consists of translating them into an integer linear program. We show two watermarking strategies: an exhaustive one based on integer linear programming constraint solving and a scalable pairing heuristic. Fingerprinting applications, for which several distinct watermarks need to be computed, benefit from the reduced computation time of our method that precomputes the watermarks only once. Moreover, we show that our method enables practical collusion-secure fingerprinting since the precomputed watermarks are based on binary alterations located at exactly the same positions. The paper includes an in-depth analysis of false-hit and false-miss occurrence probabilities for the detection algorithm. Experiments performed on our open source software WATERMILL assess the watermark robustness against common attacks and show that our method outperforms the existing ones concerning the watermark embedding speed.

Discovering implicit communities in Web forums through ontologies

Being a Community manager is an emerging employment in social Web companies. His or her role is to monitor communities on a devoted social website, in order to understand new trends or behaviours. He or she also has to discover and attract new potential users of the website in external resources like web forums, that are not necessarily on the same topics nor explicitly defined. In this paper we propose a scalable protocol to monitor on-line communications, like web forums, walls or twits. We provide an analysis method to extract implicit communities and user interests based on the semantics of data exchange and the structure of communications. The method is parameterized by a target vocabulary expressed as an ontology, in order to focus on relevant communities.

XML streams watermarking

XML streams are valuable, continuous, high-throughput sources of information whose owners must be protected against illegal redistributions. Watermarking is a known technique for hiding copyrights marks within documents, thus preventing redistributions. Here, we introduce a watermarking algorithm for Xml streams so that (i) the watermark embedding and detection processes are done online and use only a constant memory, (ii) the stream distortion is controlled, (iii) the type of the stream is preserved and finally (iv) the detection procedure does not require the original stream. We also evaluate, analytically and experimentally, the robustness of the algorithm against watermark removal attempts.

Using Hierarchical Skills for Optimized Task Assignment in Knowledge-Intensive Crowdsourcing

Besides the simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills, especially in participative science projects. In this context, there is a need to reason about the required skills for a task and the set of available skills in the crowd, in order to increase the resulting quality. Most of the existing solutions rely on unstructured tags to model skills (vector of skills). In this paper we propose to finely model tasks and participants using a skill tree, that is a taxonomy of skills equipped with a similarity distance within skills. This model of skills enables to map participants to tasks in a way that exploits the natural hierarchy among the skills. We illustrate the effectiveness of our model and algorithms through extensive experimentation with synthetic and real data sets.

Watermarking for ontologies

In this paper, we study watermarking methods to prove the ownership of an ontology. Different from existing approaches, we propose to watermark not by altering existing statements, but by removing them. Thereby, our approach does not introduce false statements into the ontology. We show how ownership of ontologies can be established with provably tight probability bounds, even if only parts of the ontology are being re-used. We finally demonstrate the viability of our approach on real-world ontologies.

Blind and squaring-resistant watermarking of vectorial building layers

Due to the ease of digital copy, watermarking is crucial to protect the intellectual property of rights owners. We propose an effective watermarking method for vectorial geographical databases, with the focus on the buildings layer. Embedded watermarks survive common geographical filters, including the essential squaring and simplification transformations, as well as deliberate removal attempts, e.g. by noise addition, cropping or over-watermarking. Robustness against the squaring transformation is not addressed by existing approaches. The impact on the quality of the data sets, defined as a composition of point accuracy and angular quality, is assessed through an extensive series of experiments. Our method is based on a quantization of the distance between the centroid of the building and its extremal vertex according to its orientation.

Multimedia and metadata watermarking driven by application constraints

Providing a fully functional multimedia DBMS (MMDBMS) becomes an emergency with the recent development of distributed environments. In this paper we address the impact of using watermarking techniques traditionally used to preserve the intellectual or industrial property (IIP) in MMDBMS. Through a multimedia content and metadata based representation model called M2, we particularly study: 1) how to watermark all components of a multimedia description, and not only its raw data 2) how watermarking can guarantee the mapping between multimedia objects and their descriptors, avoiding accidental or malevolent mismatch inside crucial documents 3) how to preserve data significance and semantics when altering data for watermarking purposes. We illustrate our approach by providing an example in the medical domain

Temporal semantic centrality for the analysis of communication networks

Understanding communication structures in huge and versatile online communities becomes a major issue. In this paper we propose a new metric, the Semantic Propagation Probability, that characterizes the users ability to propagate a concept to other users, in a rapid and focused way. The message semantics is analyzed according to a given ontology. We use this metric to obtain the Temporal Semantic Centrality of a user in the community. We propose and evaluate an efficient implementation of this metric, using real-life ontologies and data sets.