Lime: Data Lineage in the Malicious Environment

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol

Secure Watermarking for Multimedia Content Protection: A Review of its Benefits and Open Issues

Distribution channels such as digital music downloads, video-on-demand, multimedia social networks, pose new challenges to the design of content protection measures aimed at preventing copyright violations. Digital watermarking has been proposed as a possible brick of such protection systems, providing a means to embed a unique code, as a fingerprint, into each copy of the distributed content. However, application of watermarking for multimedia content protection in realistic scenarios poses several security issues. Secure signal processing, by which name we indicate a set of techniques able to process sensitive signals that have been obfuscated either by encryption or by other privacy-preserving primitives, may offer valuable solutions to the aforementioned issues. More specifically, the adoption of efficient methods for watermark embedding or detection on data that have been secured in some way, which we name in short secure watermarking, provides an elegant way to solve the security concerns of fingerprinting applications. The aim of this contribution is to illustrate recent results regarding secure watermarking to the signal processing community, highlighting both benefits and still open issues. Some of the most interesting challenges in this area, as well as new research directions, will also be discussed

Protection of an intrusion detection engine with watermarking in ad hoc networks

Mobile ad hoc networks have received great attention in recent years, mainly due to the evolution of wireless networking and mobile computing hardware. Nevertheless, many inherent vulnerabilities exist in mobile ad hoc networks and their applications that affect the security of wireless transactions. As intrusion prevention mechanisms, such as encryption and authentication, are not sufficient we need a second line of defense, Intrusion Detection. In this pa-per we present an intrusion detection engine based on neural networks and a protection method based on watermarking techniques. In particular, we exploit information visualization and machine learning techniques in order to achieve intrusion detection and we authenticate the maps produced by the application of the intelligent techniques using a novel combined watermarking embedding method. The performance of the proposed model is evaluated under different traffic conditions, mobility patterns and visualization metrics