Publications
2021
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Demo: Traffic Splitting for Tor – A Defense Against Fingerprinting Attacks.
In Proceedings of the 2021 International Conference on Networked Systems (NetSys ’21), 2021.
Abstract
Website fingerprinting (WFP) attacks on the anonymity network Tor have become ever more effective. Furthermore, research discovered that proposed defenses are insufficient or cause high overhead. In previous work, we presented a new WFP defense for Tor that incorporates multipath transmissions to repel malicious Tor nodes from conducting WFP attacks. In this demo, we showcase the operation of our traffic splitting defense by visually illustrating the underlying Tor multipath transmission using LED-equipped Raspberry Pis.
BibTex
@inproceedings{netsys-trafficsliver-demo, abstract = {Website fingerprinting (WFP) attacks on the anonymity network Tor have become ever more effective. Furthermore, research discovered that proposed defenses are insufficient or cause high overhead. In previous work, we presented a new WFP defense for Tor that incorporates multipath transmissions to repel malicious Tor nodes from conducting WFP attacks. In this demo, we showcase the operation of our traffic splitting defense by visually illustrating the underlying Tor multipath transmission using LED-equipped Raspberry Pis.}, author = {Sebastian Reuter and Jens Hiller and Jan Pennekamp and Andriy Panchenko and Klaus Wehrle}, booktitle = {Proceedings of the 2021 International Conference on Networked Systems (NetSys '21)}, code = {https://github.com/TrafficSliver/trafficsliver-net-demo}, doi = {10.14279/tuj.eceasst.80.1151}, month = {9}, title = {Demo: Traffic Splitting for Tor – A Defense against Fingerprinting Attacks}, year = {2021}, }- DOI
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2020
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Revisiting the Privacy Needs of Real-World Applicable Company Benchmarking.
In Proceedings of the 8th Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC ’20), 2020.
Abstract
Benchmarking the performance of companies is essential to identify improvement potentials in various industries. Due to a competitive environment, this process imposes strong privacy needs, as leaked business secrets can have devastating effects on participating companies. Consequently, related work proposes to protect sensitive input data of companies using secure multi-party computation or homomorphic encryption. However, related work so far does not consider that also the benchmarking algorithm, used in today's applied real-world scenarios to compute all relevant statistics, itself contains significant intellectual property, and thus needs to be protected. Addressing this issue, we present PCB -- a practical design for Privacy-preserving Company Benchmarking that utilizes homomorphic encryption and a privacy proxy -- which is specifically tailored for realistic real-world applications in which we protect companies' sensitive input data and the valuable algorithms used to compute underlying key performance indicators. We evaluate PCB's performance using synthetic measurements and showcase its applicability alongside an actual company benchmarking performed in the domain of injection molding, covering 48 distinct key performance indicators calculated out of hundreds of different input values. By protecting the privacy of all participants, we enable them to fully profit from the benefits of company benchmarking.
BibTex
@inproceedings{wahc-benchmarking, abstract = {Benchmarking the performance of companies is essential to identify improvement potentials in various industries. Due to a competitive environment, this process imposes strong privacy needs, as leaked business secrets can have devastating effects on participating companies. Consequently, related work proposes to protect sensitive input data of companies using secure multi-party computation or homomorphic encryption. However, related work so far does not consider that also the benchmarking algorithm, used in today's applied real-world scenarios to compute all relevant statistics, itself contains significant intellectual property, and thus needs to be protected. Addressing this issue, we present PCB -- a practical design for Privacy-preserving Company Benchmarking that utilizes homomorphic encryption and a privacy proxy -- which is specifically tailored for realistic real-world applications in which we protect companies' sensitive input data and the valuable algorithms used to compute underlying key performance indicators. We evaluate PCB's performance using synthetic measurements and showcase its applicability alongside an actual company benchmarking performed in the domain of injection molding, covering 48 distinct key performance indicators calculated out of hundreds of different input values. By protecting the privacy of all participants, we enable them to fully profit from the benefits of company benchmarking.}, author = {Jan Pennekamp and Patrick Sapel and Ina Berenice Fink and Simon Wagner and Sebastian Reuter and Christian Hopmann and Klaus Wehrle and Martin Henze}, booktitle = {Proceedings of the 8th Workshop on Encrypted Computing {\&} Applied Homomorphic Cryptography (WAHC '20)}, doi = {10.25835/0072999}, month = {12}, title = {Revisiting the Privacy Needs of Real-World Applicable Company Benchmarking}, year = {2020}, }- DOI
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TrafficSliver: Fighting Website Fingerprinting Attacks with Traffic Splitting.
In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS ’20), New York, NY, USA, 2020, p. 1971–85.
Abstract
Website fingerprinting (WFP) aims to infer information about the content of encrypted and anonymized connections by observing patterns of data flows based on the size and direction of packets. By collecting traffic traces at a malicious Tor entry node -- one of the weakest adversaries in the attacker model of Tor -- a passive eavesdropper can leverage the captured meta-data to reveal the websites visited by a Tor user. As recently shown, WFP is significantly more effective and realistic than assumed. Concurrently, former WFP defenses are either infeasible for deployment in real-world settings or defend against specific WFP attacks only.To limit the exposure of Tor users to WFP, we propose novel lightweight WFP defenses, TrafficSliver, which successfully counter today's WFP classifiers with reasonable bandwidth and latency overheads and, thus, make them attractive candidates for adoption in Tor. Through user-controlled splitting of traffic over multiple Tor entry nodes, TrafficSliver limits the data a single entry node can observe and distorts repeatable traffic patterns exploited by WFP attacks. We first propose a network-layer defense, in which we apply the concept of multipathing entirely within the Tor network. We show that our network-layer defense reduces the accuracy from more than 98% to less than 16% for all state-of-the-art WFP attacks without adding any artificial delays or dummy traffic. We further suggest an elegant client-side application-layer defense, which is independent of the underlying anonymization network. By sending single HTTP requests for different web objects over distinct Tor entry nodes, our application-layer defense reduces the detection rate of WFP classifiers by almost 50 percentage points. Although it offers lower protection than our network-layer defense, it provides a security boost at the cost of a very low implementation overhead and is fully compatible with today's Tor network.
BibTex
@inproceedings{ccs-trafficsliver, abstract = {Website fingerprinting (WFP) aims to infer information about the content of encrypted and anonymized connections by observing patterns of data flows based on the size and direction of packets. By collecting traffic traces at a malicious Tor entry node -- one of the weakest adversaries in the attacker model of Tor -- a passive eavesdropper can leverage the captured meta-data to reveal the websites visited by a Tor user. As recently shown, WFP is significantly more effective and realistic than assumed. Concurrently, former WFP defenses are either infeasible for deployment in real-world settings or defend against specific WFP attacks only.To limit the exposure of Tor users to WFP, we propose novel lightweight WFP defenses, TrafficSliver, which successfully counter today's WFP classifiers with reasonable bandwidth and latency overheads and, thus, make them attractive candidates for adoption in Tor. Through user-controlled splitting of traffic over multiple Tor entry nodes, TrafficSliver limits the data a single entry node can observe and distorts repeatable traffic patterns exploited by WFP attacks. We first propose a network-layer defense, in which we apply the concept of multipathing entirely within the Tor network. We show that our network-layer defense reduces the accuracy from more than 98% to less than 16% for all state-of-the-art WFP attacks without adding any artificial delays or dummy traffic. We further suggest an elegant client-side application-layer defense, which is independent of the underlying anonymization network. By sending single HTTP requests for different web objects over distinct Tor entry nodes, our application-layer defense reduces the detection rate of WFP classifiers by almost 50 percentage points. Although it offers lower protection than our network-layer defense, it provides a security boost at the cost of a very low implementation overhead and is fully compatible with today's Tor network.}, address = {New York, NY, USA}, author = {De la Cadena, Wladimir and Mitseva, Asya and Hiller, Jens and Pennekamp, Jan and Reuter, Sebastian and Filter, Julian and Engel, Thomas and Wehrle, Klaus and Panchenko, Andriy}, booktitle = {Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS '20)}, code = {https://github.com/TrafficSliver/trafficsliver-net}, doi = {10.1145/3372297.3423351}, isbn = {9781450370899}, keywords = {privacy, onion routing, website fingerprinting, anonymous communication, traffic analysis, web privacy}, location = {Virtual Event, USA}, month = {11}, numpages = {15}, pages = {1971--1985}, publisher = {Association for Computing Machinery}, series = {CCS '20}, title = {TrafficSliver: Fighting Website Fingerprinting Attacks with Traffic Splitting}, year = {2020}, }- DOI
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2019
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Multipathing Traffic to Reduce Entry Node Exposure in Onion Routing.
In 2019 IEEE 27th International Conference on Network Protocols (ICNP ’19), 2019, p. 1–2.
Abstract
Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client's identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.
BibTex
@inproceedings{icnp-multipathing-poster, abstract = {Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client's identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.}, author = {J. {Pennekamp} and J. {Hiller} and S. {Reuter} and W. {De la Cadena} and A. {Mitseva} and M. {Henze} and T. {Engel} and K. {Wehrle} and A. {Panchenko}}, booktitle = {2019 IEEE 27th International Conference on Network Protocols (ICNP '19)}, doi = {10.1109/ICNP.2019.8888029}, keywords = {Routing;Security;Internet;Wireless fidelity;Timing;Privacy;Fingerprint recognition}, location = {Chicago, IL, USA}, month = {10}, pages = {1--2}, title = {Multipathing Traffic to Reduce Entry Node Exposure in Onion Routing}, year = {2019}, }- DOI