Anonymity networks and access to information during conflicts: towards a distributed network organisation

Access to information is crucial during conflicts and other critical events such as population uprisings. An increasing number of social interactions happen in the cyberspace, while information exchanges at the infrastructural level (monitoring systems, sensor networks, etc.) are now also based on Internet and wireless links rather than ad hoc, isolated wired networks. However, the nature of the Internet allows powerful hostile actors to block, censor, or redirect communication to and from specific Internet services, through a number of available techniques. Anonymity networks such as Tor provide a way to circumvent traditional strategies for restricting access to online resources, and make communication harder to trace and identify. Tor, in particular, has been successfully used in past crises to evade censorship and Internet blockades (Egypt in 2011, and Iran in 2012). Anonymity networks can provide essential communication tools during conflicts, allowing information exchanges to be concealed from external observers, anonymised, and made resilient to imposed traffic controls and geographical restrictions. However, the design of networks such as Tor makes them vulnerable to large-scale denial of service attacks, as shown by the DDoS targeted at Tor hidden services in March 2015. In this paper, we analyse the structural weaknesses of Tor with regard to denial of service attacks, and propose a number of modifications to the structure of the Tor network aimed at improving its resilience to a large coordinated offensive run by a hostile actor in a conflict scenario. In particular, we introduce novel mechanisms that allow relay information to be propagated in a distributed and peer-to-peer manner. This eliminates the need for directory services, and allows the deployment of Tor-like networks in hostile environments, where centralised control is impossible. The proposed improvements concern the network organisation, but preserve the underlying onion routing mechanism that is at the base of Tor's anonymity

Walking Onions: Scaling Distribution of Information Safely in Anonymity Networks

Scaling anonymity networks offers unique security challenges, as attackers can exploit differing views of the network’s topology to perform epistemic and route capture attacks. Anonymity networks in practice, such as Tor, have opted for security over scalability by requiring participants to share a globally consistent view of all relays to prevent these kinds of attacks. Such an approach requires each user to maintain up-to-date information about every relay, causing the total amount of data each user must download every epoch to scale linearly with the number of relays. As the number of clients increases, more relays must be added to provide bandwidth, further exacerbating the total load on the network. In this work, we present Walking Onions, a set of protocols improving scalability for anonymity networks. Walking Onions enables constant-size scaling of the information each user must download in every epoch, even as the number of relays in the network grows. Furthermore, we show how relaxing the clients’ bandwidth growth from constant to logarithmic can enable an outsized improvement to relays’ bandwidth costs. Notably, Walking Onions offers the same security properties as current designs that require a globally consistent network view. We present two protocol variants. The first requires minimal changes from current onion-routing systems. The second presents a more significant design change, thereby reducing the latency required to establish a path through the network while providing better forward secrecy than previous such constructions. We evaluate Walking Onions against a generalized onion-routing anonymity network and discuss tradeoffs among the approaches

Correlation-Based Traffic Analysis Attacks on Anonymity Networks

In this paper, we address attacks that exploit the timing behavior of TCP and other protocols and applications in low-latency anonymity networks. Mixes have been used in many anonymous communication systems and are supposed to provide countermeasures to defeat traffic analysis attacks. In this paper, we focus on a particular class of traffic analysis attacks, flow-correlation attacks, by which an adversary attempts to analyze the network traffic and correlate the traffic of a flow over an input link with that over an output link. Two classes of correlation methods are considered, namely time-domain methods and frequency-domain methods. Based on our threat model and known strategies in existing mix networks, we perform extensive experiments to analyze the performance of mixes. We find that all but a few batching strategies fail against flow-correlation attacks, allowing the adversary to either identify ingress and egress points of a flow or to reconstruct the path used by the flow. Counterintuitively, some batching strategies are actually detrimental against attacks. The empirical results provided in this paper give an indication to designers of Mix networks about appropriate configurations and mechanisms to be used to counter flow-correlation attacks

Correlation-Based Traffic Analysis Attacks on Anonymity Networks

In this paper, we address attacks that exploit the timing behavior of TCP and other protocols and applications in low-latency anonymity networks. Mixes have been used in many anonymous communication systems and are supposed to provide countermeasures to defeat traffic analysis attacks. In this paper, we focus on a particular class of traffic analysis attacks, flow-correlation attacks, by which an adversary attempts to analyze the network traffic and correlate the traffic of a flow over an input link with that over an output link. Two classes of correlation methods are considered, namely time-domain methods and frequency-domain methods. Based on our threat model and known strategies in existing mix networks, we perform extensive experiments to analyze the performance of mixes. We find that all but a few batching strategies fail against flow-correlation attacks, allowing the adversary to either identify ingress and egress points of a flow or to reconstruct the path used by the flow. Counterintuitively, some batching strategies are actually detrimental against attacks. The empirical results provided in this paper give an indication to designers of Mix networks about appropriate configurations and mechanisms to be used to counter flow-correlation attacks

A Framework for anonymous background data delivery and feedback

The current state of the industry’s methods of collecting background data reflecting diagnostic and usage information are often opaque and require users to place a lot of trust in the entity receiving the data. For vendors, having a centralized database of potentially sensitive data is a privacy protection headache and a potential liability should a breach of that database occur. Unfortunately, high profile privacy failures are not uncommon, so many individuals and companies are understandably skeptical and choose not to contribute any information. It is a shame, since the data could be used for improving reliability, or getting stronger security, or for valuable academic research into real-world usage patterns. We propose, implement and evaluate a framework for non-realtime anonymous data collection, aggregation for analysis, and feedback. Departing from the usual “trusted core” approach, we aim to maintain reporters’ anonymity even if the centralized part of the system is compromised. We design a peer-to-peer mix network and its protocol that are tuned to the properties of background diagnostic traffic. Our system delivers data to a centralized repository while maintaining (i) source anonymity, (ii) privacy in transit, and (iii) the ability to provide analysis feedback back to the source. By removing the core’s ability to identify the source of data and to track users over time, we drastically reduce its attractiveness as a potential attack target and allow vendors to make concrete and verifiable privacy and anonymity claims

ANDaNA: Anonymous Named Data Networking Application

Content-centric networking -- also known as information-centric networking (ICN) -- shifts emphasis from hosts and interfaces (as in today's Internet) to data. Named data becomes addressable and routable, while locations that currently store that data become irrelevant to applications. Named Data Networking (NDN) is a large collaborative research effort that exemplifies the content-centric approach to networking. NDN has some innate privacy-friendly features, such as lack of source and destination addresses on packets. However, as discussed in this paper, NDN architecture prompts some privacy concerns mainly stemming from the semantic richness of names. We examine privacy-relevant characteristics of NDN and present an initial attempt to achieve communication privacy. Specifically, we design an NDN add-on tool, called ANDaNA, that borrows a number of features from Tor. As we demonstrate via experiments, it provides comparable anonymity with lower relative overhead.Comment: NDSS 2012 - Proceedings of the Network and Distributed System Security Symposium, San Diego, California, US

Neyman-Pearson Decision in Traffic Analysis

The increase of encrypted traffic on the Internet may become a problem for network-security applications such as intrusion-detection systems or interfere with forensic investigations. This fact has increased the awareness for traffic analysis, i.e., inferring information from communication patterns instead of its content. Deciding correctly that a known network flow is either the same or part of an observed one can be extremely useful for several network-security applications such as intrusion detection and tracing anonymous connections. In many cases, the flows of interest are relayed through many nodes that reencrypt the flow, making traffic analysis the only possible solution. There exist two well-known techniques to solve this problem: passive traffic analysis and flow watermarking. The former is undetectable but in general has a much worse performance than watermarking, whereas the latter can be detected and modified in such a way that the watermark is destroyed. In the first part of this dissertation we design techniques where the traffic analyst (TA) is one end of an anonymous communication and wants to deanonymize the other host, under this premise that the arrival time of the TA\u27s packets/requests can be predicted with high confidence. This, together with the use of an optimal detector, based on Neyman-Pearson lemma, allow the TA deanonymize the other host with high confidence even with short flows. We start by studying the forensic problem of leaving identifiable traces on the log of a Tor\u27s hidden service, in this case the used predictor comes in the HTTP header. Afterwards, we propose two different methods for locating Tor hidden services, the first one is based on the arrival time of the request cell and the second one uses the number of cells in certain time intervals. In both of these methods, the predictor is based on the round-trip time and in some cases in the position inside its burst, hence this method does not need the TA to have access to the decrypted flow. The second part of this dissertation deals with scenarios where an accurate predictor is not feasible for the TA. This traffic analysis technique is based on correlating the inter-packet delays (IPDs) using a Neyman-Pearson detector. Our method can be used as a passive analysis or as a watermarking technique. This algorithm is first made robust against adversary models that add chaff traffic, split the flows or add random delays. Afterwards, we study this scenario from a game-theoretic point of view, analyzing two different games: the first deals with the identification of independent flows, while the second one decides whether a flow has been watermarked/fingerprinted or not

Low-latency mix networks for anonymous communication

Every modern online application relies on the network layer to transfer information, which exposes the metadata associated with digital communication. These distinctive characteristics encapsulate equally meaningful information as the content of the communication itself and allow eavesdroppers to uniquely identify users and their activities. Hence, by exposing the IP addresses and by analyzing patterns of the network traffic, a malicious entity can deanonymize most online communications. While content confidentiality has made significant progress over the years, existing solutions for anonymous communication which protect the network metadata still have severe limitations, including centralization, limited security, poor scalability, and high-latency. As the importance of online privacy increases, the need to build low-latency communication systems with strong security guarantees becomes necessary. Therefore, in this thesis, we address the problem of building multi-purpose anonymous networks that protect communication privacy. To this end, we design a novel mix network Loopix, which guarantees communication unlinkability and supports applications with various latency and bandwidth constraints. Loopix offers better security properties than any existing solution for anonymous communications while at the same time being scalable and low-latency. Furthermore, we also explore the problem of active attacks and malicious infrastructure nodes, and propose a Miranda mechanism which allows to efficiently mitigate them. In the second part of this thesis, we show that mix networks may be used as a building block in the design of a private notification system, which enables fast and low-cost online notifications. Moreover, its privacy properties benefit from an increasing number of users, meaning that the system can scale to millions of clients at a lower cost than any alternative solution

SoK: Metadata-Protecting Communication Systems

Protecting metadata of communications has been an area of active research since the dining cryptographers problem was introduced by David Chaum in 1988. The Snowden revelations from 2013 resparked research in this direction. Consequently over the last decade we have witnessed a flurry of novel systems designed to protect metadata of users\u27 communications online. However, such systems leverage different assumptions and design choices to achieve their goal; resulting in a scattered view of the desirable properties, potential vulnerabilities, and limitations of existing metadata-protecting communication systems (MPCS). In this work we survey 31 systems targeting metadata-protected communications, and present a unified view of the current state of affairs. We provide two different taxonomies for existing MPCS, first into four different categories by the precise type of metadata protections they offer, and next into six families based on the core techniques that underlie them. By contrasting these systems we identify potential vulnerabilities, as well as subtle privacy implications of design choices of existing MPCS. Furthermore, we identify promising avenues for future research for MPCS, and desirable properties that merit more attention

Practical Private Information Retrieval

In recent years, the subject of online privacy has been attracting much interest, especially as more Internet users than ever are beginning to care about the privacy of their online activities. Privacy concerns are even prompting legislators in some countries to demand from service providers a more privacy-friendly Internet experience for their citizens. These are welcomed developments and in stark contrast to the practice of Internet censorship and surveillance that legislators in some nations have been known to promote. The development of Internet systems that are able to protect user privacy requires private information retrieval (PIR) schemes that are practical, because no other efficient techniques exist for preserving the confidentiality of the retrieval requests and responses of a user from an Internet system holding unencrypted data. This thesis studies how PIR schemes can be made more relevant and practical for the development of systems that are protective of users' privacy. Private information retrieval schemes are cryptographic constructions for retrieving data from a database, without the database (or database administrator) being able to learn any information about the content of the query. PIR can be applied to preserve the confidentiality of queries to online data sources in many domains, such as online patents, real-time stock quotes, Internet domain names, location-based services, online behavioural profiling and advertising, search engines, and so on. In this thesis, we study private information retrieval and obtain results that seek to make PIR more relevant in practice than all previous treatments of the subject in the literature, which have been mostly theoretical. We also show that PIR is the most computationally efficient known technique for providing access privacy under realistic computation powers and network bandwidths. Our result covers all currently known varieties of PIR schemes. We provide a more detailed summary of our contributions below: Our first result addresses an existing question regarding the computational practicality of private information retrieval schemes. We show that, unlike previously argued, recent lattice-based computational PIR schemes and multi-server information-theoretic PIR schemes are much more computationally efficient than a trivial transfer of the entire PIR database from the server to the client (i.e., trivial download). Our result shows the end-to-end response times of these schemes are one to three orders of magnitude (10--1000 times) smaller than the trivial download of the database for realistic computation powers and network bandwidths. This result extends and clarifies the well-known result of Sion and Carbunar on the computational practicality of PIR. Our second result is a novel approach for preserving the privacy of sensitive constants in an SQL query, which improves substantially upon the earlier work. Specifically, we provide an expressive data access model of SQL atop of the existing rudimentary index- and keyword-based data access models of PIR. The expressive SQL-based model developed results in between 7 and 480 times improvement in query throughput than previous work. We then provide a PIR-based approach for preserving access privacy over large databases. Unlike previously published access privacy approaches, we explore new ideas about privacy-preserving constraint-based query transformations, offline data classification, and privacy-preserving queries to index structures much smaller than the databases. This work addresses an important open problem about how real systems can systematically apply existing PIR schemes for querying large databases. In terms of applications, we apply PIR to solve user privacy problem in the domains of patent database query and location-based services, user and database privacy problems in the domain of the online sales of digital goods, and a scalability problem for the Tor anonymous communication network. We develop practical tools for most of our techniques, which can be useful for adding PIR support to existing and new Internet system designs