Confidential Guarantee in Network Flow using Obfuscation Scheme

IEEE ACM Transactions on Networking, 2015

Large datasets of real network flows acquired from the Internet are an invaluable resource for the research community. Applications include network modeling and simulation, identification of security attacks, and validation of research results. Unfortunately, network flows carry extremely sensitive information, and this discourages the publication of those datasets. Indeed, existing techniques for network flow sanitization are vulnerable to different kinds of attacks, and solutions proposed for microdata anonymity cannot be directly applied to network traces. In our previous research, we proposed an obfuscation technique for network flows, providing formal confidentiality guarantees under realistic assumptions about the adversary's knowledge. In this paper, we identify the threats posed by the incremental release of network flows, we propose a novel defense algorithm, and we formally prove the achieved confidentiality guarantees. An extensive experimental evaluation of the algorithm for incremental obfuscation, carried out with billions of real Internet flows, shows that our obfuscation technique preserves the utility of flows for network traffic analysis.

2012

In the last decade, the release of network flows has gained significant popularity among researchers and networking communities. Indeed, network flows are a fundamental tool for modeling the network behavior, identifying security attacks, and validating research results. Unfortunately, due to the sensitive nature of network flows, security and privacy concerns discourage the publication of such datasets. On the one hand, existing techniques proposed to sanitize network flows do not provide any formal guarantees. On the other hand, microdata anonymization techniques are not directly applicable to network flows. In this paper, we propose a novel obfuscation technique for network flows that provides formal guarantees under realistic assumptions about the adversary's knowledge. Our work is supported by extensive experiments with a large set of real network flows collected at an important Italian Tier II Autonomous System, hosting sensitive government and corporate sites. Experimental results show that our obfuscation technique preserves the utility of network flows for network traffic analysis.

2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017

Network traffic analysis reveals important information even when messages are encrypted. We consider active traffic analysis via flow fingerprinting by invisibly embedding information into packet timings of flows. In particular, assume Alice wishes to embed fingerprints into flows of a set of network input links, whose packet timings are modeled by Poisson processes, without being detected by a watchful adversary Willie. Bob, who receives the set of fingerprinted flows after they pass through the network modeled as a collection of independent and parallel M/M/1 queues, wishes to extract Alice's embedded fingerprints to infer the connection between input and output links of the network. We consider two scenarios: 1) Alice embeds fingerprints in all of the flows; 2) Alice embeds fingerprints in each flow independently with probability p. Assuming that the flow rates are equal, we calculate the maximum number of flows in which Alice can invisibly embed fingerprints while having those fingerprints successfully decoded by Bob. Then, we extend the construction and analysis to the case where flow rates are distinct, and discuss the extension of the network model.

Proceedings of the Second European Workshop on System Security - EUROSEC '09, 2009

Network traces of Internet attacks are among the most valuable resources for network analysts and security researchers. However, organizations and researchers are usually reluctant to share their network data, as network packets may contain private or sensitive information. To alleviate the problem of information leakage, network traces are often anonymized before being shared. Typical anonymization approaches sanitize, or in some cases completely remove, certain packet header fields, higher-level protocol fields, or even payload information that could reveal the source and destination of an attack incident.

2006 IEEE International Conference on Communications, 2006

Lack of trust is one of the main reasons for the limited cooperation between different organizations. The privacy of users is of paramount importance to administrators and organizations, which are reluctant to cooperate between each other and exchange network traffic traces. The main reasons behind reluctance to exchange monitored data are the protection of the users' privacy and the fear of information leakage about the internal infrastructure. Anonymization is the technique to overcome this reluctance and enhance the cooperation between different organizations with the smooth exchange of monitored data. Today, several organizations provide network traffic traces that are anonymized by software utilities or ad-hoc solutions that offer limited flexibility. The result of this approach is the creation of unrealistic traces, inappropriate for use in evaluation experiments. Furthermore, the need for fast on-line anonymization has recently emerged as cooperative defense mechanisms have to share network traffic. Our effort focuses on the design and implementation of a generic and flexible anonymization framework that provides extended functionality, covering multiple aspects of anonymization needs and allowing fine-tuning of privacy protection level. The proposed framework is composed by an anonymization application programming interface (AAPI). The performance results show that AAPI outperforms existing tools, while offering significantly more anonymization primitives.

Computer Communication Review, 2006

Releasing network measurement data-including packet tracesto the research community is a virtuous activity that promotes solid research. However, in practice, releasing anonymized packet traces for public use entails many more vexing considerations than just the usual notion of how to scramble IP addresses to preserve privacy. Publishing traces requires carefully balancing the security needs of the organization providing the trace with the research usefulness of the anonymized trace. In this paper we recount our experiences in (i) securing permission from a large site to release packet header traces of the site's internal traffic, (ii) implementing the corresponding anonymization policy, and (iii) validating its correctness. We present a general tool, tcpmkpub, for anonymizing traces, discuss the process used to determine the particular anonymization policy, and describe the use of meta-data accompanying the traces to provide insight into features that have been obfuscated by anonymization.

Proceedings of the 17th …, 2008

We analyze several recent schemes for watermarking network flows based on splitting the flow into intervals. We show that this approach creates time dependent correlations that enable an attack that combines multiple watermarked flows. Such an attack can easily be mounted in nearly all applications of network flow watermarking, both in anonymous communication and stepping stone detection. The attack can be used to detect the presence of a watermark, recover the secret parameters, and remove the watermark from a flow. The attack can be effective even if different the watermarks in different flows carry different messages.

Journal of Sensor and Actuator Networks, 2021

Statistical traffic analysis has absolutely exposed the privacy of supposedly secure network traffic, proving that encryption is not effective anymore. In this work, we present an optimal countermeasure to prevent an adversary from inferring users’ online activities, using traffic analysis. First, we formulate analytically a constrained optimization problem to maximize network traffic obfuscation while minimizing overhead costs. Then, we provide OPriv, a practical and efficient algorithm to solve dynamically the non-linear programming (NLP) problem, using Cplex optimization. Our heuristic algorithm selects target applications to mutate to and the corresponding packet length, and subsequently decreases the security risks of statistical traffic analysis attacks. Furthermore, we develop an analytical model to measure the obfuscation system’s resilience to traffic analysis attacks. We suggest information theoretic metrics for quantitative privacy measurement, using entropy. The full pri...

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