Performance and Security Improvements for Tor: A Survey

Tor [Dingledine et al. 2004] is the most widely used anonymity network today, serving millions of users on a daily basis using a growing number of volunteer-run routers. Since its deployment in 2003, there have been more than three dozen proposals that aim to improve its performance, security, and unobservability. Given the significance of this research area, our goal is to provide the reader with the state of current research directions and challenges in anonymous communication systems, focusing on the Tor network.We shed light on the design weaknesses and challenges facing the network and point out unresolved issues

Privacy in the Smart City - Applications, Technologies, Challenges and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities

A Survey on Routing in Anonymous Communication Protocols

The Internet has undergone dramatic changes in the past 15 years, and now forms a global communication platform that billions of users rely on for their daily activities. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy, ranging from profiling of users for monetizing personal information to nearly omnipotent governmental surveillance. As a result, public interest in systems for anonymous communication has drastically increased. Several such systems have been proposed in the literature, each of which offers anonymity guarantees in different scenarios and under different assumptions, reflecting the plurality of approaches for how messages can be anonymously routed to their destination. Understanding this space of competing approaches with their different guarantees and assumptions is vital for users to understand the consequences of different design options. In this work, we survey previous research on designing, developing, and deploying systems for anonymous communication. To this end, we provide a taxonomy for clustering all prevalently considered approaches (including Mixnets, DC-nets, onion routing, and DHT-based protocols) with respect to their unique routing characteristics, deployability, and performance. This, in particular, encompasses the topological structure of the underlying network; the routing information that has to be made available to the initiator of the conversation; the underlying communication model; and performance-related indicators such as latency and communication layer. Our taxonomy and comparative assessment provide important insights about the differences between the existing classes of anonymous communication protocols, and it also helps to clarify the relationship between the routing characteristics of these protocols, and their performance and scalability

Guard Sets for Onion Routing

“Entry” guards protect the Tor onion routing system from variants of the “predecessor” attack, that would allow an adversary with control of a fraction of routers to eventually de-anonymize some users. Research has however shown the three guard scheme has drawbacks and Dingledine et al. proposed in 2014 for each user to have a single long-term guard. We first show that such a guard selection strategy would be optimal if the Tor network was failure-free and static. However under realistic failure conditions the one guard proposal still suffers from the classic fingerprinting attacks, uniquely identifying users. Furthermore, under dynamic network conditions using single guards offer smaller anonymity sets to users of fresh guards. We propose and analyze an alternative guard selection scheme by way of grouping guards together to form shared guard sets. We compare the security and performance of guard sets with the three guard scheme and the one guard proposal. We show guard sets do provide increased resistance to a number of attacks, while foreseeing no significant degradation in performance or bandwidth utilization

Technical Privacy Metrics: a Systematic Survey

The file attached to this record is the author's final peer reviewed versionThe goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over eighty privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement

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

Stadium: A Distributed Metadata-Private Messaging System

Private communication over the Internet remains a challenging problem. Even if messages are encrypted, it is hard to deliver them without revealing metadata about which pairs of users are communicating. Scalable anonymity systems, such as Tor, are susceptible to traffic analysis attacks that leak metadata. In contrast, the largest-scale systems with metadata privacy require passing all messages through a small number of providers, requiring a high operational cost for each provider and limiting their deployability in practice. This paper presents Stadium, a point-to-point messaging system that provides metadata and data privacy while scaling its work efficiently across hundreds of low-cost providers operated by different organizations. Much like Vuvuzela, the current largest-scale metadata-private system, Stadium achieves its provable guarantees through differential privacy and the addition of noisy cover traffic. The key challenge in Stadium is limiting the information revealed from the many observable traffic links of a highly distributed system, without requiring an overwhelming amount of noise. To solve this challenge, Stadium introduces techniques for distributed noise generation and differentially private routing as well as a verifiable parallel mixnet design where the servers collaboratively check that others follow the protocol. We show that Stadium can scale to support 4X more users than Vuvuzela using servers that cost an order of magnitude less to operate than Vuvuzela nodes

On the Privacy of Sublinear-Communication Jaccard Index Estimation via Min-hash Sketching

The min-hash sketch is a well-known technique for low-communication approximation of the Jaccard index between two input sets. Moreover, there is a folklore belief that min-hash sketch based protocols protect the privacy of the inputs. In this paper, we investigate this folklore to quantify the privacy of the min-hash sketch. We begin our investigation by considering the privacy of min-hash in a centralized setting where the hash functions are chosen by the min-hash functionality and are unknown to the participants. We show that in this case the min-hash output satisfies the standard definition of differential privacy (DP) without any additional noise. This immediately yields a privacy-preserving sublinear-communication semi-honest 2-PC protocol based on FHE where the hash function is evaluated homomorphically. To improve the efficiency of this protocol, we next consider an implementation in the random oracle model. Here, the protocol participants jointly sample public prefixes for domain separation of the random oracle, and locally evaluate the resulting hash functions on their input sets. Unfortunately, we show that in this public hash function setting, the min-hash output is no longer DP. We therefore consider the notion of distributional differential privacy (DDP) introduced by Bassily et al.~(FOCS 2013). We show that if the honest party\u27s set has sufficiently high min-entropy then the output of the min-hash functionality achieves DDP, again without any added noise. This yields a more efficient semi-honest two-party protocol in the random oracle model, where parties first locally hash their input sets and then perform a 2PC for comparison. By proving that our protocols satisfy DP and DDP respectively, our results formally confirm and qualify the folklore belief that min-hash based protocols protect the privacy of their inputs

Enhancing Privacy and Fairness in Search Systems

Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.Nach einer Zeit schneller Fortschritte in den Fähigkeiten digitaler Systeme beginnt die Gesellschaft zu erkennen, dass Systeme, die Menschen bei verschiedenen Aufgaben unterstützen sollen, den Einzelnen und die Gesellschaft auch schädigen können. Suchsysteme haben ein erhebliches Potenzial, um zu solchen unerwünschten Ergebnissen beizutragen, weil sie den Zugang zu Informationen vermitteln und explizit oder implizit Menschen in immer mehr Anwendungen in Ranglisten anordnen. Da sie riesige Datenmengen sowohl über Suchende als auch über Gesuchte sammeln, können sie die Privatsphäre dieser beiden Benutzergruppen verletzen. In Anwendungen, in denen Ranglisten einen Einfluss auf den finanziellen Lebensunterhalt der Menschen außerhalb der Plattform haben, z. B. auf Sharing-Economy-Plattformen oder Jobbörsen, haben Suchmaschinen eine immense wirtschaftliche Macht über ihre Nutzer, indem sie die Sichtbarkeit von Personen in Suchergebnissen kontrollieren. In dieser Dissertation werden neue Modelle und Methoden entwickelt, die verschiedene Aspekte der Privatsphäre und der Fairness in Suchsystemen, sowohl für Suchende als auch für Gesuchte, abdecken. Insbesondere leistet die Arbeit folgende Beiträge: (1) Wir schlagen ein Modell für die Berechnung von fairen Rankings vor, bei denen Suchsubjekte entsprechend ihrer Relevanz angezeigt werden. Die Sichtbarkeit wird im Laufe der Zeit durch ein Optimierungsmodell adjustiert, um die Verzerrungen der Sichtbarkeit für Sucher zu kompensieren, während die Nützlichkeit des Rankings beibehalten bleibt. (2) Wir schlagen ein Modell für die Bestimmung kritischer Suchanfragen vor, in dem für jeden Nutzer Aanfragen, die zu seinem Nutzerprofil in den Top-k-Suchergebnissen führen, herausgefunden werden. Das Problem der Berechnung von exponierenden Suchanfragen wird als Reverse-Nearest-Neighbor-Suche modelliert. Solche kritischen Suchanfragen werden dann von einem Learning-to-Rank-Modell geordnet, um die sensitiven Suchanfragen herauszufinden. (3) Wir schlagen ein Modell zur Quantifizierung von Risiken für die Privatsphäre aus Textdaten in Online Communities vor. Die Methode baut auf einem Themenmodell auf, bei dem jedes Thema durch einen Crowdsourcing-Sensitivitätswert annotiert wird. Die Risiko-Scores sind mit der Relevanz eines Benutzers mit kritischen Themen verbunden. Wir schlagen Relevanzmaße vor, die unterschiedliche Dimensionen des Benutzerinteresses an einem Thema erfassen, und wir zeigen, wie diese Maße mit der Risikowahrnehmung von Menschen korrelieren. (4) Wir schlagen ein Modell für personalisierte Suche vor, in dem die Privatsphäre geschützt wird. In dem Modell werden Suchanfragen von Nutzer partitioniert und in synthetische Profile eingefügt. Das Modell erreicht einen guten Kompromiss zwischen der Suchsystemnützlichkeit und der Privatsphäre, indem semantisch kohärente Fragmente der Suchhistorie innerhalb einzelner Profile beibehalten werden, wobei gleichzeitig angestrebt wird, die Ähnlichkeit der synthetischen Profile mit den ursprünglichen Nutzerprofilen zu minimieren. Die Modelle werden mithilfe von Informationssuchtechniken und Nutzerstudien ausgewertet. Wir benutzen eine Vielzahl von Datensätzen, die von Abfrageprotokollen über soziale Medien Postings und die Fragen vom Q&A Forums bis hin zu Artikellistungen von Sharing-Economy-Plattformen reichen

Evaluation of Trust in the Internet Of Things: Models, Mechanisms And Applications

In the blooming era of the Internet of Things (IoT), trust has become a vital factor for provisioning reliable smart services without human intervention by reducing risk in autonomous decision making. However, the merging of physical objects, cyber components and humans in the IoT infrastructure has introduced new concerns for the evaluation of trust. Consequently, a large number of trust-related challenges have been unsolved yet due to the ambiguity of the concept of trust and the variety of divergent trust models and management mechanisms in different IoT scenarios. In this PhD thesis, my ultimate goal is to propose an efficient and practical trust evaluation mechanisms for any two entities in the IoT. To achieve this goal, the first important objective is to augment the generic trust concept and provide a conceptual model of trust in order to come up with a comprehensive understanding of trust, influencing factors and possible Trust Indicators (TI) in the context of IoT. Following the catalyst, as the second objective, a trust model called REK comprised of the triad Reputation, Experience and Knowledge TIs is proposed which covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation, personal experiences to global opinions. The mathematical models and evaluation mechanisms for the three TIs in the REK trust model are proposed. Knowledge TI is as “direct trust” rendering a trustor’s understanding of a trustee in respective scenarios that can be obtained based on limited available information about characteristics of the trustee, environment and the trustor’s perspective using a variety of techniques. Experience and Reputation TIs are originated from social features and extracted based on previous interactions among entities in IoT. The mathematical models and calculation mechanisms for the Experience and Reputation TIs also proposed leveraging sociological behaviours of humans in the real-world; and being inspired by the Google PageRank in the web-ranking area, respectively. The REK Trust Model is also applied in variety of IoT scenarios such as Mobile Crowd-Sensing (MCS), Car Sharing service, Data Sharing and Exchange platform in Smart Cities and in Vehicular Networks; and for empowering Blockchain-based systems. The feasibility and effectiveness of the REK model and associated evaluation mechanisms are proved not only by the theoretical analysis but also by real-world applications deployed in our ongoing TII and Wise-IoT projects