# Towards provably invisible network flow fingerprints

@article{Soltani2017TowardsPI, title={Towards provably invisible network flow fingerprints}, author={Ramin Soltani and Dennis L. Goeckel and Donald F. Towsley and Amir Houmansadr}, journal={2017 51st Asilomar Conference on Signals, Systems, and Computers}, year={2017}, pages={258-262} }

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…

## 10 Citations

Fundamental Limits of Invisible Flow Fingerprinting

- Computer ScienceIEEE Transactions on Information Forensics and Security
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Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections by incorporating independent Poisson processes.

Fundamental Limits of Covert Bit Insertion in Packets

- Computer Science2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
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This paper proves that Alice can covertly insert on average $O(c(n)/\sqrt{n})$ bits in a sequence of n packets, where c(n)$ is the average number of conditional pmf of packet sizes given the history, with a support of at least size two.

Asymptotic Loss in Privacy due to Dependency in Gaussian Traces

- Computer Science2019 IEEE Wireless Communications and Networking Conference (WCNC)
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The requirements on anonymization to thwart such statistical matching are established, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.

Fundamental Limits of Covert Packet Insertion

- Computer ScienceIEEE Transactions on Communications
- 2020

The fundamental limits for covert communications via packet insertion over packet channels whose packet timings are governed by a renewal process of rate are considered and it is shown that in a stream of packets transmitted by Jack, Alice can covertly insert N packets.

Matching Anonymized and Obfuscated Time Series to Users’ Profiles

- Computer Science, MathematicsIEEE Transactions on Information Theory
- 2019

It is demonstrated that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.

Privacy of Dependent Users Against Statistical Matching

- Computer ScienceIEEE Transactions on Information Theory
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It is shown that inter-user dependency is disastrous to privacy, and any non-negligible dependency between users significantly reduces the effectiveness of anonymization and obfuscation schemes.

Asymptotic Limits of Privacy in Bayesian Time Series Matching

- Computer Science, Mathematics2019 53rd Annual Conference on Information Sciences and Systems (CISS)
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This paper obtains the theoretical bounds on user privacy for situations in which user traces are matchable to sequences of prior behavior, despite anonymization of data time series.

Covert Wireless Communication With Artificial Noise Generation

- Computer ScienceIEEE Transactions on Wireless Communications
- 2018

A strategy where the friendly node closest to the adversary, without close coordination with Alice, produces artificial noise is proposed, which allows Alice to reliably and covertly send bits to Bob when other “friendly” nodes distributed according to a two-dimensional Poisson point process are present.

Consumer Life Cycle and Profiling: A Data Mining Perspective

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With the development of technology and continuously increasing of the market demand, the concept to produce better merchandises is generated in the companies. Each customer wants an individual…

INFORMATION-THEORETIC LIMITS ON STATISTICAL MATCHING WITH APPLICATIONS TO PRIVACY

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