Digital provenance - models, systems, and applications

Data provenance refers to the history of creation and manipulation of a data object and is being widely used in various application domains including scientific experiments, grid computing, file and storage system, streaming data etc. However, existing provenance systems operate at a single layer of abstraction (workflow/process/OS) at which they record and store provenance whereas the provenance captured from different layers provide the highest benefit when integrated through a unified provenance framework. To build such a framework, a comprehensive provenance model able to represent the provenance of data objects with various semantics and granularity is the first step. In this thesis, we propose a such a comprehensive provenance model and present an abstract schema of the model. ^ We further explore the secure provenance solutions for distributed systems, namely streaming data, wireless sensor networks (WSNs) and virtualized environments. We design a customizable file provenance system with an application to the provenance infrastructure for virtualized environments. The system supports automatic collection and management of file provenance metadata, characterized by our provenance model. Based on the proposed provenance framework, we devise a mechanism for detecting data exfiltration attack in a file system. We then move to the direction of secure provenance communication in streaming environment and propose two secure provenance schemes focusing on WSNs. The basic provenance scheme is extended in order to detect packet dropping adversaries on the data flow path over a period of time. We also consider the issue of attack recovery and present an extensive incident response and prevention system specifically designed for WSNs

On the Secrecy of Spread-Spectrum Flow Watermarks

Spread-spectrum flow watermarks offer an invisible and ready-to-use flow watermarking scheme that can be employed to stealthily correlate the two ends of a network communication. Such technique has wide applications in network security and privacy. Although several methods have been proposed to detect various flow watermarks, few can effectively detect spread-spectrum flow watermarks. Moreover, there is currently no solution that allows end users to eliminate spread-spectrum flow watermarks from their flows without the support of a separate network element. In this paper, we propose a novel approach to detect spread-spectrum flow watermarks by leveraging their intrinsic features. Contrary to the common belief that Pseudo-Noise (PN) codes can render flow watermarks invisible, we prove that PN codes actually facilitate their detection. Furthermore, we propose a novel method based on TCP’s flow-control mechanism that provides end users with the ability to autonomously remove spread-spectrum flow watermarks. We conducted extensive experiments on traffic flowing both through one-hop proxies in the PlanetLab network, and through Tor. The experimental results show that the proposed detection system can achieve up to 100% detection rate with zero false positives, and confirm that our elimination system can effectively remove spread-spectrum flow watermarks

On the Secrecy of Spread-Spectrum Flow Watermarks

Spread-spectrum flow watermarks offer an invisible and ready-to-use flow watermarking scheme that can be employed to stealthily correlate the two ends of a network communication. Such technique has wide applications in network security and privacy. Although several methods have been proposed to detect various flow watermarks, few can effectively detect spread-spectrum flow watermarks. Moreover, there is currently no solution that allows end users to eliminate spread-spectrum flow watermarks from their flows without the support of a separate network element. In this paper, we propose a novel approach to detect spread-spectrum flow watermarks by leveraging their intrinsic features. Contrary to the common belief that Pseudo-Noise (PN) codes can render flow watermarks invisible, we prove that PN codes actually facilitate their detection. Furthermore, we propose a novel method based on TCP’s flow-control mechanism that provides end users with the ability to autonomously remove spread-spectrum flow watermarks. We conducted extensive experiments on traffic flowing both through one-hop proxies in the PlanetLab network, and through Tor. The experimental results show that the proposed detection system can achieve up to 100% detection rate with zero false positives, and confirm that our elimination system can effectively remove spread-spectrum flow watermarks