Preserving Both Privacy and Utility in Network Trace Anonymization

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We present the general approach and instantiate it based on CryptoPAn. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The results show that our approach can significantly reduce the level of information leakage (e.g., less than 1\% of the information leaked by CryptoPAn) with comparable utility

ToR K-Anonymity against deep learning watermarking attacks

It is known that totalitarian regimes often perform surveillance and censorship of their communication networks. The Tor anonymity network allows users to browse the Internet anonymously to circumvent censorship filters and possible prosecution. This has made Tor an enticing target for state-level actors and cooperative state-level adversaries, with privileged access to network traffic captured at the level of Autonomous Systems(ASs) or Internet Exchange Points(IXPs). This thesis studied the attack typologies involved, with a particular focus on traffic correlation techniques for de-anonymization of Tor endpoints. Our goal was to design a test-bench environment and tool, based on recently researched deep learning techniques for traffic analysis, to evaluate the effectiveness of countermeasures provided by recent ap- proaches that try to strengthen Tor’s anonymity protection. The targeted solution is based on K-anonymity input covert channels organized as a pre-staged multipath network. The research challenge was to design a test-bench environment and tool, to launch active correlation attacks leveraging traffic flow correlation through the detection of in- duced watermarks in Tor traffic. To de-anonymize Tor connection endpoints, our tool analyses intrinsic time patterns of Tor synthetic egress traffic to detect flows with previ- ously injected time-based watermarks. With the obtained results and conclusions, we contributed to the evaluation of the security guarantees that the targeted K-anonymity solution provides as a countermeasure against de-anonymization attacks.Já foi extensamente observado que em vários países governados por regimes totalitários existe monitorização, e consequente censura, nos vários meios de comunicação utilizados. O Tor permite aos seus utilizadores navegar pela internet com garantias de privacidade e anonimato, de forma a evitar bloqueios, censura e processos legais impostos pela entidade que governa. Estas propriedades tornaram a rede Tor um alvo de ataque para vários governos e ações conjuntas de várias entidades, com acesso privilegiado a extensas zonas da rede e vários pontos de acesso à mesma. Esta tese realiza o estudo de tipologias de ataques que quebram o anonimato da rede Tor, com especial foco em técnicas de correlação de tráfegos. O nosso objetivo é realizar um ambiente de estudo e ferramenta, baseada em técnicas recentes de aprendizagem pro- funda e injeção de marcas de água, para avaliar a eficácia de contramedidas recentemente investigadas, que tentam fortalecer o anonimato da rede Tor. A contramedida que pre- tendemos avaliar é baseada na criação de multi-circuitos encobertos, recorrendo a túneis TLS de entrada, de forma a acoplar o tráfego de um grupo anonimo de K utilizadores. A solução a ser desenvolvida deve lançar um ataque de correlação de tráfegos recorrendo a técnicas ativas de indução de marcas de água. Esta ferramenta deve ser capaz de correla- cionar tráfego sintético de saída de circuitos Tor, realizando a injeção de marcas de água à entrada com o propósito de serem detetadas num segundo ponto de observação. Aplicada a um cenário real, o propósito da ferramenta está enquadrado na quebra do anonimato de serviços secretos fornecidos pela rede Tor, assim como os utilizadores dos mesmos. Os resultados esperados irão contribuir para a avaliação da solução de anonimato de K utilizadores mencionada, que é vista como contramedida para ataques de desanonimi- zação

Strengthening Privacy and Cybersecurity through Anonymization and Big Data

L'abstract è presente nell'allegato / the abstract is in the attachmen

Privacy-preserving network monitoring at high-speed

Network monitoring represents a key step for several applications, such as cyber-security and traffic engineering. Examples of the data include packet traces captured in the network and log files obtained from services like the DNS and BGP. It is widely known that monitoring may expose privacy-sensitive information. Deep packet inspection, for example, exposes the destination servers contacted by users, and non-encrypted fields of certain protocols, such as Service Name Indication (SNI) in TLS handshakes. New privacy regulations (e.g. GDPR) impose strict rules when handling data that carry privacy-sensitive information. They guarantee the protection of personal data, provide the interested parties certain rights, and assign powers to the regulators to enforce them. As network monitoring data carries information that reveals users' identity, it must be treated in the light of these regulations. Network monitoring infrastructure must guarantee that sensitive information is not leaked or, preferably, must not collect any unnecessary data that may threat users' privacy. Historically, the solution to these problems has been anonymization -- i.e., replacing sensitive fields with obfuscated copies. This approach however has two drawbacks: First, anonymization reduces the value of the collected information. For instance, while anonymizing client and server IP addresses in traffic logs helps to protect privacy, it renders it impossible to evaluate particular services that could be identified by their server IP addresses. Second, anonymization of protocol fields in isolation is not sufficient, as users' identity might be revealed by subtler techniques. For example, even if one obfuscates the client IP addresses in DNS traffic logs, the set of hostnames resolved by a client (if exposed in the logs) may still help to uncover identities. We are building a flexible tool that exposes to monitors only the information strictly required, thus reducing at the source risks to people's privacy. Our solution satisfies three requirements: (i)~it automatically searches for protocol fields that can be linked to particular users; (ii)~it anonymizes information considering all protocol stack, and uses a stateful approach, employing k-anonymization algorithms; (iii)~it is light-weight and scalable, thus deployable in high-speed links at multiple Gb/s. Our solution is based on the Intel Data Plane Development Kit, a set of libraries and drivers for fast packet processing. We have built a prototype that is deployed in a campus network. At the present, the prototype is able to handle multiple 10~Gb/s links with zero packet losses, performing several anonymization steps on packets. Anonymized packets are forwarded to legacy monitoring systems that receive information already deprived of privacy sensitive fields. We are testing k-anonymization approaches to perform selective anonymization of sensitive fields, such as TLS SNIs and server IP addresses, with the aim to obfuscate only cases in which the information helps to uncover users behind the traffic. In this poster we will present our architecture and system design, as well as show preliminary results of the prototype deployment

α-MON: Anonymized Passive Traffic Monitoring

Packet measurements are essential for several applications, such as cyber-security, accounting and troubleshooting. They, however, threaten privacy by exposing sensitive information. Anonymization has been the answer to this challenge, i.e., replacing sensitive information by obfuscated copies. Anonymization of packet traces, however, comes with some drawbacks. First, it reduces the value of data. Second, it requires to consider diverse protocols because information may leak from many non-encrypted fields. Third, it must be performed at high speeds directly at the monitor, to prevent private data from leaking, calling for real-time solutions.We present α-MON, a flexible tool for privacy-preserving packet monitoring. It replicates input packet streams to different consumers while anonymizing values according to flexible policies that cover all protocol layers. Beside classic anonymization mechanisms such as IP address obfuscation, α-MON supports α-anonymization, a novel solution to obfuscate values that can be uniquely traced back to limited sets of users. Differently from classic anonymization approaches, α-anonymity works on a streaming fashion, with zero delay, operating at high-speed links on a packet-by-packet basis. We evaluate α-MON performance using packet traces collected from an ISP network. Results show that it enables α-anonymity in real-time. α-MON is available to the community as an open-source project

TORKAMELEON. IMPROVING TOR’S CENSORSHIP RESISTANCE WITH K-ANONYMIZATION MEDIA MORPHING COVERT INPUT CHANNELS

Anonymity networks such as Tor and other related tools are powerful means of increas- ing the anonymity and privacy of Internet users’ communications. Tor is currently the most widely used solution by whistleblowers to disclose confidential information and denounce censorship measures, including violations of civil rights, freedom of expres- sion, or guarantees of free access to information. However, recent research studies have shown that Tor is vulnerable to so-called powerful correlation attacks carried out by global adversaries or collaborative Internet censorship parties. In the Tor ”arms race” scenario, we can see that as new censorship, surveillance, and deep correlation tools have been researched, new, improved solutions for preserving anonymity have also emerged. In recent research proposals, unobservable encapsulation of IP packets in covert media channels is one of the most promising defenses against such threat models. They leverage WebRTC-based covert channels as a robust and practical approach against powerful traf- fic correlation analysis. At the same time, these solutions are difficult to combat through the traffic-blocking measures commonly used by censorship authorities. In this dissertation, we propose TorKameleon, a censorship evasion solution de- signed to protect Tor users with increased censorship resistance against powerful traffic correlation attacks executed by global adversaries. The system is based on flexible K- anonymization input circuits that can support TLS tunneling and WebRTC-based covert channels before forwarding users’ original input traffic to the Tor network. Our goal is to protect users from machine and deep learning correlation attacks between incom- ing user traffic and observed traffic at different Tor network relays, such as middle and egress relays. TorKameleon is the first system to implement a Tor pluggable transport based on parameterizable TLS tunneling and WebRTC-based covert channels. We have implemented the TorKameleon prototype and performed extensive validations to ob- serve the correctness and experimental performance of the proposed solution in the Tor environment. With these evaluations, we analyze the necessary tradeoffs between the performance of the standard Tor network and the achieved effectiveness and performance of TorKameleon, capable of preserving the required unobservability properties.Redes de anonimização como o Tor e soluções ou ferramentas semelhantes são meios poderosos de aumentar a anonimidade e a privacidade das comunicações de utilizadores da Internet . O Tor é atualmente a rede de anonimato mais utilizada por delatores para divulgar informações confidenciais e denunciar medidas de censura tais como violações de direitos civis e da liberdade de expressão, ou falhas nas garantias de livre acesso à informação. No entanto, estudos recentes mostram que o Tor é vulnerável a adversários globais ou a entidades que colaboram entre si para garantir a censura online. Neste cenário competitivo e de jogo do “gato e do rato”, é possível verificar que à medida que novas soluções de censura e vigilância são investigadas, novos sistemas melhorados para a preservação de anonimato são também apresentados e refinados. O encapsulamento de pacotes IP em túneis encapsulados em protocolos de media são uma das mais promissoras soluções contra os novos modelos de ataque à anonimidade. Estas soluções alavancam canais encobertos em protocolos de media baseados em WebRTC para resistir a poderosos ataques de correlação de tráfego e a medidas de bloqueios normalmente usadas pelos censores. Nesta dissertação propomos o TorKameleon, uma solução desenhada para protoger os utilizadores da rede Tor contra os mais recentes ataques de correlação feitos por um modelo de adversário global. O sistema é baseado em estratégias de anonimização e reencaminhamento do tráfego do utilizador através de K nós, utilizando também encap- sulamento do tráfego em canais encobertos em túneis TLS ou WebRTC. O nosso objetivo é proteger os utilizadores da rede Tor de ataques de correlação implementados através de modelos de aprendizagem automática feitos entre o tráfego do utilizador que entra na rede Tor e esse mesmo tráfego noutro segmento da rede, como por exemplo nos nós de saída da rede. O TorKameleon é o primeiro sistema a implementar um Tor pluggable transport parametrizável, baseado em túneis TLS ou em canais encobertos em protocolos media. Implementamos um protótipo do sistema e realizamos uma extensa avalição expe- rimental, inserindo a solução no ambiente da rede Tor. Com base nestas avaliações, anali- zamos o tradeoff necessário entre a performance da rede Tor e a eficácia e a performance obtida do TorKameleon, que garante as propriedades de preservação de anonimato

TorKameleon: Improving Tor's Censorship Resistance With K-anonymization and Media-based Covert Channels

Anonymity networks like Tor greatly improve online privacy but are susceptible to correlation attacks from state-level adversaries and Internet censors. To enhance privacy, covert channels encapsulated in media protocols, particularly WebRTC-based encapsulation, have shown promise against passive traffic correlation attacks. However, their effectiveness against active correlation attacks has not been explored, and compatibility with Tor remains limited. This paper introduces TorKameleon, a censorship evasion solution that protects Tor users from passive and active correlation attacks. It incorporates K-anonymization techniques to fragment and reroute traffic through multiple paths formed by multiple proxies and uses covert WebRTC-based channels or TLS tunnels to encapsulate user traffic. The developed prototype has undergone extensive validation for performance and resilience against correlation attacks, showcasing its effectiveness

α-MON: Traffic Anonymizer for Passive Monitoring

Packet measurements at scale are essential for several applications, such as cyber-security, accounting and troubleshooting. They, however, threaten users’ privacy by exposing sensitive information. Anonymization has been the answer to this challenge, i.e., replacing sensitive information with obfuscated copies. Anonymization of packet traces, however, comes with some challenges and drawbacks. First, it reduces the value of data. Second, it requires to consider diverse protocols because information may leak from many non-encrypted fields. Third, it must be performed at high speeds directly at the monitor, to prevent private data from leaking, calling for real-time solutions. We present , a flexible tool for privacy-preserving packet monitoring. It replicates input packet streams to different consumers while anonymizing protocol fields according to flexible policies that cover all protocol layers. Beside classic anonymization mechanisms such as IP address obfuscation, supports z-anonymization, a novel solution to obfuscate rare values that can be uniquely traced back to limited sets of users. Differently from classic anonymization approaches, works on a streaming fashion, with zero delay, operating at high-speed links on a packet-by-packet basis. We quantify the impact of on traffic measurements, finding that it introduces minimal error when it comes to finding heavy-hitter services. We evaluate performance using packet traces collected from an ISP network and show that it achieves a sustainable rate of 40 Gbit/s on a Commercial Off-the Shelf server. is available to the community as an open-source project

A Covert Data Transport Protocol

Both enterprise and national firewalls filter network connections. For data forensics and botnet removal applications, it is important to establish the information source. In this paper, we describe a data transport layer which allows a client to transfer encrypted data that provides no discernible information regarding the data source. We use a domain generation algorithm (DGA) to encode AES encrypted data into domain names that current tools are unable to reliably differentiate from valid domain names. The domain names are registered using (free) dynamic DNS services. The data transmission format is not vulnerable to Deep Packet Inspection (DPI).Comment: 8 pages, 10 figures, conferenc