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

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

Anonymity-Preserving Public-Key Encryption: A Constructive Approach

Abstract. A receiver-anonymous channel allows a sender to send a message to a receiver without an adversary learning for whom the message is intended. Wireless broadcast channels naturally provide receiver anonymity, as does multi-casting one message to a receiver population containing the intended receiver. While anonymity and confidentiality appear to be orthogonal properties, making anonymous communication confidential is more involved than one might expect, since the ciphertext might reveal which public key has been used to encrypt. To address this problem, public-key cryptosystems with enhanced security properties have been proposed. We investigate constructions as well as limitations for preserving receiver anonymity when using public-key encryption (PKE). We use the constructive cryptography approach by Maurer and Renner and interpret cryptographic schemes as constructions of a certain ideal resource (e.g. a confidential anonymous channel) from given real resources (e.g. a broadcast channel). We define appropriate anonymous communication resources and show that a very natural resource can be constructed by using a PKE scheme which fulfills three properties that appear in cryptographic literature (IND-CCA, key-privacy, weak robustness). We also show that a desirable stronger variant, preventing the adversary from selective “trial-deliveries ” of messages, is unfortunately unachievable by any PKE scheme, no matter how strong. The constructive approach makes the guarantees achieved by applying a cryptographic scheme explicit in the constructed (ideal) resource; this specifies the exact requirements for the applicability of a cryptographic scheme in a given context. It also allows to decide which of the existing security properties of such a cryptographic scheme are adequate for the considered scenario, and which are too weak or too strong. Here, we show that weak robustness is necessary but that so-called strong robustness is unnecessarily strong in that it does not construct a (natural) stronger resource

Succinct Oblivious RAM

As online storage services become increasingly common, it is important that users\u27 private information is protected from database access pattern analyses. Oblivious RAM (ORAM) is a cryptographic primitive that enables users to perform arbitrary database accesses without revealing any information about the access pattern to the server. Previous ORAM studies focused mostly on reducing the access overhead. Consequently, the access overhead of the state-of-the-art ORAM constructions are almost at practical levels in certain application scenarios such as secure processors. However, we assume that the server space usage could become a new important issue in the coming big-data era. To enable large-scale computation in security-aware settings, it is necessary to rethink the ORAM server space cost using big-data standards. In this paper, we introduce "succinctness" as a theoretically tractable and practically relevant criterion of the ORAM server space efficiency in the big-data era. We, then, propose two succinct ORAM constructions that also exhibit state-of-the-art performance in terms of the bandwidth blowup and the user space. We also give non-asymptotic analyses and simulation results which indicate that the proposed ORAM constructions are practically effective

Directional Privacy for Deep Learning

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in the training of deep learning models. This applies isotropic Gaussian noise to gradients during training, which can perturb these gradients in any direction, damaging utility. Metric DP, however, can provide alternative mechanisms based on arbitrary metrics that might be more suitable. In this paper we apply \textit{directional privacy}, via a mechanism based on the von Mises-Fisher (VMF) distribution, to perturb gradients in terms of \textit{angular distance} so that gradient direction is broadly preserved. We show that this provides $\epsilon d$-privacy for deep learning training, rather than the $(\epsilon, \delta)$-privacy of the Gaussian mechanism; and that experimentally, on key datasets, the VMF mechanism can outperform the Gaussian in the utility-privacy trade-off

Combining Differential Privacy and Secure Multiparty Computation

We consider how to perform privacy-preserving analyses on private data from different data providers and containing personal information of many different individuals. We combine differential privacy and secret sharing in the same system to protect the privacy of both the data providers and the individuals. We have implemented a prototype of this combination and the overhead of adding differential privacy to secret sharing is small enough to be usable in practice

REMOVING THE MASK: VIDEO FINGERPRINTING ATTACKS OVER TOR

The Onion Router (Tor) is used by adversaries and warfighters alike to encrypt session information and gain anonymity on the internet. Since its creation in 2002, Tor has gained popularity by terrorist organizations, human traffickers, and illegal drug distributors who wish to use Tor services to mask their identity while engaging in illegal activities. Fingerprinting attacks assist in thwarting these attempts. Website fingerprinting (WF) attacks have been proven successful at linking a user to the website they have viewed over an encrypted Tor connection. With consumer video streaming traffic making up a large majority of internet traffic and sites like YouTube remaining in the top visited sites in the world, it is just as likely that adversaries are using videos to spread misinformation, illegal content, and terrorist propaganda. Video fingerprinting (VF) attacks look to use encrypted network traffic to predict the content of encrypted video sessions in closed- and open-world scenarios. This research builds upon an existing dataset of encrypted video session data and use statistical analysis to train a machine-learning classifier, using deep fingerprinting (DF), to predict videos viewed over Tor. DF is a machine learning technique that relies on the use of convolutional neural networks (CNN) and can be used to conduct VF attacks against Tor. By analyzing the results of these experiments, we can more accurately identify malicious video streaming activity over Tor.CivilianApproved for public release. Distribution is unlimited

Anatomy of a Vulnerable Fitness Tracking System: Dissecting the Fitbit Cloud, App, and Firmware

Funding: This work has been co-funded by the DFG as part of projects S1 within the CRC 1119 CROSSING and C.1 within the RTG 2050 ”Privacy and Trust for Mobile Users”, and by the BMBF within CRISP. Paul Patras has been partially supported by the Scottish Informatics and Computer Science Alliance (SICSA) through a PECE grant.Fitbit fitness trackers record sensitive personal information, including daily step counts, heart rate profiles, and locations visited. By design, these devices gather and upload activity data to a cloud service, which provides aggregate statistics to mobile app users. The same principles govern numerous other Internet-of-Things (IoT) services that target different applications. As a market leader, Fitbit has developed perhaps the most secure wearables architecture that guards communication with end-to-end encryption. In this paper, we analyze the complete Fitbit ecosystem and, despite the brand's continuous efforts to harden its products, we demonstrate a series of vulnerabilities with potentially severe implications to user privacy and device security. We employ a repertoire of techniques encompassing protocol analysis, software decompiling, and both static and dynamic embedded code analysis, to reverse engineer previously undocumented communication semantics, the official smartphone app, and the tracker firmware. Through this interplay and in-depth analysis, we reveal how attackers can exploit the Fitbit protocol to extract private information from victims without leaving a trace, and wirelessly flash malware without user consent. We demonstrate that users can tamper with both the app and firmware to selfishly manipulate records or circumvent Fitbit's walled garden business model, making the case for an independent, user-controlled, and more secure ecosystem. Finally, based on the insights gained, we make specific design recommendations that not only can mitigate the identified vulnerabilities, but are also broadly applicable to securing future wearable system architectures.PostprintPeer reviewe