Video data has become central in many modern systems, from public surveillance to autonomous vehicles, but its widespread use raises serious concerns about exposing people’s identities and behaviors. This survey takes a structured look at how recent research has tried to address these concerns through video anonymization. We conduct a systematic review of the literature and organize existing work into a taxonomy that separates different anonymization strategies, the visual regions they target, and the assumptions they make about the video setting. By examining these categories, we highlight how current methods approach privacy protection and where they tend to fall short. A consistent pattern across the field is that most studies are designed for single-view videos, even though many real environments involve multiple synchronized cameras. Only a handful of works consider this multi-perspective setting, revealing a clear disconnect between research and real-world needs. We also review the datasets and evaluation metrics commonly used in anonymization studies and show that they rarely capture multi-view complexity, underscoring the need for more representative benchmarks. Finally, we discuss the utility goals that anonymization methods aim to preserve and outline key gaps that future work must address to support practical, multi-camera video applications.

Human motion is a behavioral biometric trait that can be used to identify individuals and infer private attributes such as medical conditions. This poses a serious threat to privacy as motion extraction from video and motion capture are increasingly used for a variety of applications, including mixed reality, robotics, medicine, and the quantified self. In order to protect the privacy of the tracked individuals, anonymization techniques that preserve the utility of the data are required. However, anonymizing motion data is a challenging task because there are many dependencies in motion sequences (such as physiological constraints) that, if ignored, make the anonymized motion sequence appear unnatural. In this paper, we propose Pantomime, a full-body anonymization technique for motion data, which uses foundation motion models to generate motion sequences that adhere to the dependencies in the data, thus keeping the utility of the anonymized data high. Our results show that Pantomime can maintain the naturalness of the motion sequences while reducing the identification accuracy to 10%.

Mix networks are a cornerstone of anonymous communication, protecting users' relationships by relaying their messages through a series of mix nodes. To accommodate large payloads, deployed systems rely on message fragmentation, but this seemingly benign feature unwittingly opens a subtle door for adversaries. In this paper, we show that fragmentation enables adversaries to tag messages by suppressing single fragments and thereby break sender-recipient unlinkability, striking at the core privacy guarantee of mix networks. We demonstrate the practicality of this attack on Nym, a real-world deployed mix network. To address this threat, we design a lightweight mitigation that incurs only little overhead in both packet size and processing time. We provide a formal model of fragmentation in mix networks and prove that our mitigation restores unlinkability in this model.

Smart cities promise safer streets, smoother traffic, and more efficient services, enabled by dense networks of urban sensors. Yet this infrastructure, often unnoticed by citizens, introduces pervasive privacy risks, from tracking, profiling, and sensitive inferences to subtle forms of self-censorship. Despite widespread deployment, little is known about how the public understands and perceives these sensing systems. To address this gap, we present an intervention based user study (n = 172) in which participants are exposed to data collection by six urban sensors, including cameras and alternative technologies commonly framed as privacy-preserving. Participants encounter either the sensors alone or sensors accompanied by real-time data visualizations. Our results reveal widespread misunderstanding of some sensors (radar, LiDAR, Wi-Fi, depth, and thermal imaging sensors), particularly their capacity for identification and for attribute inferences such as gender or age. We also identify persistent misconceptions, including the belief that Wi-Fi poses privacy risks only when users connect to public networks. While making sensors visible and visualizing collected data improves privacy awareness, these measures alone are not enough for citizens to understand the actual risks of urban sensing. We derive recommendations for privacy-respecting smart city environments grounded in citizens’ informational needs and expectations.

Anonymous communication is vital for enabling individuals to participate in social discourse without fear of marginalization or persecution. An important but often overlooked part of anonymous communication is the bootstrapping of new communication channels. If Alice wants to communicate with Bob, she must first learn his in-system identifier. In synchronous designs, message exchange is only possible once both communication partners have agreed to communicate. Thus, Alice must notify Bob of her intent, Bob must learn her in-system identifier, and Bob must acknowledge her notification. This bootstrapping process is generally assumed to occur out-of-band, but if it discloses metadata, communication partners are revealed even if the channel itself is fully anonymized. We propose Sabot, the first anonymous bootstrapping protocol that achieves both strong cryptographic privacy guarantees and bandwidth-efficient communication. In Sabot, clients cooperatively generate a private relationship matrix, which encodes who wants to contact whom. Clients communicate with k ≥ 2 servers to obtain ''their'' part of the matrix and augment the received information using Private Information Retrieval (PIR) to learn about their prospective communication partners. Compared to previous solutions, Sabot achieves stronger privacy guarantees and reduces the bandwidth overhead by an order of magnitude.

Beamforming, as introduced in WiFi 5, requires clients to broadcast observations of their channel characteristics. This introduces a new information source for WiFi sensing with privacy threats that have not been explored, so far. With WiFi networks being ubiquitous in our everyday lives, the impact of unknown privacy threats is likely severe. To investigate this concern, we introduce BFId, the first identity inference attack using BFI-based sensing and evaluate its efficacy on a novel dataset containing WiFi recordings of 197 individuals. We show that we can infer the identity of individuals with very high accuracy, across different walking styles and perspectives, even with large sample sizes.

The convergence of 6G and WiFi technologies promises unprecedented online connectivity, enabling immersive experiences such as the Metaverse, with a particular emphasis on VR gaming. Despite these advancements, they bring to light considerable privacy concerns, especially the ability of adversaries to deduce personal information from encrypted gaming network traffic. Focusing on VR headsets and Nintendo Switch consoles, this study explores the privacy implications within such network environments. By simulating the typical network conditions of online multiplayer games, we expose potential privacy breaches by adversaries from both WiFi and WAN, including mobile service providers. Classical machine learning algorithms can successfully classify games and gaming consoles with an accuracy exceeding 90%, further dissecting the network traffic to unveil distinct signatures and assess privacy risks.

Mix networks allow communication with strong anonymity guarantees. In theory, mix networks can scale indefinitely, as additional nodes can be added to the network to support new users. However, one factor that limits scalability in current designs is the need for all clients to know both the identity and the key of every available mix node. In circuit-based onion routing, a mechanism that does not require this knowledge to be globally available exists, but it relies on the interactivity of the circuit construction to keep its security guarantees. We therefore set out to investigate whether we can transfer such a mechanism to the context of message-based mix networks. In this paper, we propose Aimless Onions, the first mix format that enables clients to create onions in a mix network without knowing which nodes are available. Rather than downloading topology information, clients only need to acquire constant-size public parameters. Thus, Aimless Onions overcomes an important scalability limitation in mix networks, while retaining the same security guarantees as the state of the art. Using Aimless Onions, clients sending 25 messages per hour save 74% of bandwidth compared to using Spinx packets and topology information download, even at today's network sizes.

Privacy risks in differentially private (DP) systems increase significantly when data is correlated, as standard DP metrics often underestimate the resulting privacy leakage, leaving sensitive information vulnerable. Given the ubiquity of dependencies in real-world databases, this oversight poses a critical challenge for privacy protections. Bayesian differential privacy (BDP) extends DP to account for these correlations, yet current BDP mechanisms indicate a notable utility loss, limiting its adoption. In this work, we address whether BDP can be realistically implemented in common data structures without sacrificing utility—a key factor for its applicability. By analyzing arbitrary and structured correlation models, including Gaussian multivariate distributions and Markov chains, we derive practical utility guarantees for BDP. Our contributions include theoretical links between DP and BDP and a novel methodology to adapt DP mechanisms to meet the requirements of BDP. Through evaluations on real-world databases, we demonstrate that our novel theorems enable the design of BDP mechanisms that maintain competitive utility, paving the way for practical privacy-preserving data practices in correlated settings.

Research on the effects of essential hyperparameters of DP-SGD lacks consensus, verification, and replication. Contradictory and anecdotal statements on their influence make matters worse. While DP-SGD is the standard optimization algorithm for privacy-preserving machine learning, its adoption is still commonly challenged by low performance compared to non-private learning approaches. As proper hyperparameter settings can improve the privacy-utility trade-off, understanding the influence of the hyperparameters promises to simplify their optimization towards better performance, and likely foster acceptance of private learning.To shed more light on these influences, we conduct a replication study: We synthesize extant research on hyperparameter influences of DP-SGD into conjectures, conduct a dedicated factorial study to independently identify hyperparameter effects, and assess which conjectures can be replicated across multiple datasets, model architectures, and differential privacy budgets. While we cannot (consistently) replicate conjectures about the main and interaction effects of the batch size and the number of epochs, we were able to replicate the conjectured relationship between the clipping threshold and learning rate. Furthermore, we were able to quantify the significant importance of their combination compared to the other hyperparameters.

The composition theorems of differential privacy (DP) allow data curators to combine different algorithms to obtain a new algorithm that continues to satisfy DP. However, new granularity notions (i.e., neigh-borhood definitions), data domains, and composition settings have appeared in the literature that the classical composition theorems do not cover. For instance, the original parallel composition theorem does not translate well to general granularity notions. This complicates the opportunity of composing DP mechanisms in new settings and obtaining accurate estimates of the incurred privacy loss after composition. To overcome these limitations, we study the composability of DP in a general framework and for any kind of data domain or neighborhood definition. We give a general composition theorem in both independent and adaptive versions and we provide analogous composition results for approximate, zero-concentrated, and Gaussian DP. Besides, we study the hypothesis needed to obtain the best composition bounds. Our theorems cover both parallel and sequential composition settings. Importantly, they also cover every setting in between, allowing us to compute the final privacy loss of a composition with greatly improved accuracy.

Using virtualized network management functions, the Service Based Architecture will replace the Reference Point Architecture for managing future mobile networks. It naturally lacks a clear security perimeter and has an increased attack surface, so defending the control plane against attacks requires a novel protection paradigm. Both National Institute of Standards and Technology and 3rd Generation Partnership Project suggest moving from perimeter security to a Zero Trust Architecture (ZTA), authenticating all request initiators and controlling access to all resources for each request. However, it insofar remains somewhat unclear to which extent the suggested management protocols do indeed meet the ZTA. We are exploring the standardized communication management protocols in this paper. Our analysis indicates that with careful implementation, the existing network functions and protocols can indeed achieve comprehensive authentication and access control so that the ZTA can be met.

As part of the most recent mobile broadband standards, 5G and 6G, network slicing solutions will shape the future of networks. By employing network slicing, network operators can protect participants against different attacks over the network by isolating devices and providing resource guarantees. Currently, to evaluate new network-slicing solutions, network researchers or engineers have to either evaluate the solution in a virtual environment that can not accurately scale to the performance of hardware platforms or invest significant portions of time in network configuration on hardware platforms. To quickly prototype network slicing solutions and other solutions depending on software-defined networking (SDN) features, we introduce a hardware abstraction layer featuring a vendor-independent API to manage hardware platforms and a new testbed solution featuring topologies similar to common network evaluation platforms like mininet.In this demo, we showcase a network-slicing solution leveraging SDN. The audience can visually observe the performance of the communication channel before and after denial-of-service attacks and with and without the secure network slicing.

Mix networks are a well-known technique to hide communication metadata, but incur a high overhead especially in group communication settings. This hinders their adoption in real-world usage, as group communication makes up a big part of modern communication patterns. In this paper, we introduce "PolySphinx", a mix format that is a step towards efficient anonymous multicasting and allows a mix node to replicate the message payload to multiple recipients. We prove that PolySphinx does not compromise on the anonymity offered to users, while considerably reducing the latency of group messages: In a group with 25 members, the average latency drops from 6.1s using the state-of-the-art Rollercoaster approach to 4.1s using PolySphinx.

Biometric systems based on brain activity have been proposed as an alternative to passwords or to complement current authentication techniques. By leveraging the unique brainwave patterns of individuals, these systems offer the possibility of creating authentication solutions that are resistant to theft, hands-free, accessible, and potentially even revocable. However, despite the growing stream of research in this area, faster advance is hindered by reproducibility problems. Issues such as the lack of standard reporting schemes for performance results and system configuration, or the absence of common evaluation benchmarks, make comparability and proper assessment of different biometric solutions challenging. Further, barriers are erected to future work when, as so often, source code is not published open access. To bridge this gap, we introduce NeuroIDBench, a flexible open source tool to benchmark brainwave-based authentication models. It incorporates nine diverse datasets, implements a comprehensive set of pre-processing parameters and machine learning algorithms, enables testing under two common adversary models (known vs unknown attacker), and allows researchers to generate full performance reports and visualizations. We use NeuroIDBench to investigate the shallow classifiers and deep learning-based approaches proposed in the literature, and to test robustness across multiple sessions. We observe a 37.6% reduction in Equal Error Rate (EER) for unknown attacker scenarios (typically not tested in the literature), and we highlight the importance of session variability to brainwave authentication. All in all, our results demonstrate the viability and relevance of NeuroIDBench in streamlining fair comparisons of algorithms, thereby furthering the advancement of brainwave-based authentication through robust methodological practices.

Biometric data contains distinctive human traits such as facial features or gait patterns. The use of biometric data permits an individuation so exact that the data is utilized effectively in identification and authentication systems. But for this same reason, privacy protections become indispensably necessary. Privacy protection is extensively afforded by the technique of anonymization. Anonymization techniques protect sensitive personal data from biometrics by obfuscating or removing information that allows linking records to the generating individuals, to achieve high levels of anonymity. However, our understanding and possibility to develop effective anonymization relies, in equal parts, on the effectiveness of the methods employed to evaluate anonymization performance. In this paper, we assess the state-of-the-art methods used to evaluate the performance of anonymization techniques for facial images and for gait patterns. We demonstrate that the state-of-the-art evaluation methods have serious and frequent shortcomings. In particular, we find that the underlying assumptions of the state-of-the-art are quite unwarranted. State-of-the-art methods generally assume a difficult recognition scenario and thus a weak adversary. However, that assumption causes state-of-the-art evaluations to grossly overestimate the performance of the anonymization. Therefore, we propose a strong adversary which is aware of the anonymization in place. This adversary model implements an appropriate measure of anonymization performance. We improve the selection process for the evaluation dataset, and we reduce the numbers of identities contained in the dataset while ensuring that these identities remain easily distinguishable from one another. Our novel evaluation methodology surpasses the state-of-the-art because we measure worst-case performance and so deliver a highly reliable evaluation of biometric anonymization techniques.

Onion routing and mix networks are a central technology to enable anonymous communication on the Internet. As such, a large number of protocols and model variants have been explored in the field, which offer differing levels of privacy, exhibit vulnerabilities, or even supersede each other. These factors make discovering the appropriate formalization for new developments difficult, and some model variants have not been formalized at all. We address this issue by creating one parametrized framework that encompasses the onion routing and mix network models and functionalities with a global adversary in the related work. In doing so, we create a categorization of the variants of onion routing models in use in the related work and map common OR and mix network protocols to their variants. For each identified variant: Our framework offers i) an ideal functionality in the Universal Composability framework, and ii) game-based properties that imply realization of the ideal functionality when a protocol satisfies them. In effect, our framework both unifies and extends previous formalization efforts in the field.

Onion routing and mix networks are fundamental concepts to provide users with anonymous access to the Internet. Various corresponding solutions rely on the Sphinx packet format. However, flaws in Sphinx's underlying proof strategy were found recently. It is thus currently unclear which guarantees Sphinx actually provides, and, even worse, there is no suitable proof strategy available. In this paper, we restore the security foundation for all these works by building an analytical framework for Sphinx. We discover that the previously-used Decisional Diffie-Hellman (DDH) assumption is insufficient for a security proof and show that the Gap Diffie-Hellman (GDH) assumption is required instead. We apply it to prove that a slightly adapted version of the Sphinx packet format is secure under the GDH assumption. We are thus, to the best of our knowledge, the first to provide a detailed, in-depth security proof for Sphinx that holds. Our adaptations to Sphinx are necessary, as we demonstrate with an attack on sender privacy that would otherwise be possible in Sphinx's adversary model.

Face images are a rich source of information that can be used to identify individuals and infer private information about them. To mitigate this privacy risk, anonymizations employ transformations on clear images to obfuscate sensitive information, all while retaining some utility. Albeit published with impressive claims, they sometimes are not evaluated with convincing methodology. Reversing anonymized images to resemble their real input --- and even be identified by face recognition approaches --- represents the strongest indicator for flawed anonymization. Some recent results indeed indicate that this is possible for some approaches. It is, however, not well understood, which approaches are reversible, and why. In this paper, we provide an exhaustive investigation in the phenomenon of face anonymization reversibility. Among other things, we find that 11 out of 15 tested face anonymizations are at least partially reversible and highlight how both reconstruction and inversion are the underlying processes that make reversal possible.

Brainwave measuring devices have transitioned from specialized medical tools to user-friendly and economically accessible consumer products. This shift has opened new avenues for pervasive services, with applications spanning brain-computer interfaces (BCIs), disease detection, criminal trials, and, notably, authentication in computer security. Electroencephalography (EEG) signals, being difficult to steal and revocable, present an attractive biometric option. However, the practical deployment of these signals is hindered by security threats, usability issues, and privacy concerns. To this end, we expect to improve the overall performance of authentication systems using consumer-grade devices, gain a better understanding of user attitudes toward this type of authentication, and protect the user's privacy against unauthorized use of samples collected during enrollment and verification.

With the advent of consumer wearables that capture brain activity, the use of brainwaves to verify a user's identity has been proposed as a convenient alternative to passwords. While recent work on brain biometrics shows feasible performance, it falls short in considering practical applicability. We propose a new solution, BrainNet, which trains a Siamese Network to measure the similarity of two electroencephalogram (EEG) inputs, and uses time-locked brain reactions instead of continuous mental activity to improve accuracy. This approach removes the need for retraining the brainwave recognition system, a common pitfall in current solutions, facilitating practical deployment. Furthermore, BrainNet achieves Equal Error Rates (EERs) of 0.14% in verification mode and 0.34% in identification mode, outperforming the state of the art even when evaluated under unseen attacker scenarios.

Data collection and aggregation by online services happens to an extent that is often beyond awareness and comprehension of its users. Transparency tools become crucial to inform people, though it is unclear how well they work. To investigate this matter, we conducted a user study focusing on Facebook, which has recently released the 'Off-Facebook Activity' transparency dashboard that informs about personal data collection from third parties. We exposed a group of n = 100 participants to the dashboard and surveyed their level of awareness and reactions to understand how transparency impacts users' privacy attitudes and intended behavior. Our participants were surprised about the massive amount of collected data, became significantly less comfortable with data collection, and more likely to take protective measures. Collaterally, we observed that current consent schemes are inadequate. Based on the survey findings, we make recommendations for more usable transparency and highlight the need to raise awareness about transparency tools and to provide easily actionable privacy controls.

Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality gait recording techniques, such as depth cameras or motion capture suits, an increasing amount of detailed gait data is captured and processed. The introduction and rise of the Metaverse is an example of a potentially popular application scenario in which the gait of users is transferred onto digital avatars. As a first step towards developing effective anonymization techniques for high-quality gait data, we study different aspects of movement data to quantify their contribution to gait recognition. We first extract categories of features from the literature on human gait perception and then design experiments for each category to assess how much the information they contain contributes to recognition success. We evaluated the utility of gait perturbation by means of naturalness ratings in a user study. Our results show that gait anonymization will be challenging, as the data is highly redundant and inter-dependent.

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users' paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking and synthetic-generation measures for trajectory databases with syntactic and differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories.

Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.

Automated accounts affect political discourse in online social networks and therefore pose a threat to public opinion. They manipulate the regular flow of discussions by, for example, spreading false news, polluting content, or changing the popularity of users/content. Meanwhile, 56% of online social network users express concern about online false news. Therefore, reliable detection of automated accounts, among other things, is crucial for protecting political discourse in our society. However, the task is complex and challenging. Recent studies show that state-of-the-art bot detection algorithms have severe generalization problems.In this paper, we study features for reliable generalized identification of bot behavior on Twitter. Therefore, we propose an ensemble model that combines multiple neural network architectures. In a preprocessing step, we vectorize tweet texts using BERTweet. Exploiting behavioral features, the model then uncovers patterns in the metadata via a feed-forward and a convolutional component and incorporates the vectorized tweet texts via a recurrent unit. Extensive Leave-One-Botnet-Out (LOBO) evaluations on 20 real-world bot data sets show state-of-the-art performance in all experiments and outperform related approaches in average and peak performance.

While most anonymization technology available today is designed for static and small data, the current picture is of massive volumes of dynamic data arriving at unprecedented velocities. From the standpoint of anonymization, the most challenging type of dynamic data is data streams. However, while the majority of proposals deal with publishing either count-based or aggregated statistics about the underlying stream, little attention has been paid to the problem of continuously publishing the stream itself with differential privacy guarantees. In this work, we propose an anonymization method that can publish multiple numerical-attribute, finite microdata streams with high protection as well as high utility, the latter aspect measured as data distortion, delay and record reordering. Our method, which relies on the well-known differential pulse-code modulation scheme, adapts techniques originally intended for hybrid video encoding, to favor and leverage dependencies among the blocks of the original stream and thereby reduce data distortion. The proposed solution is assessed experimentally on two of the largest data sets in the scientific community working in data anonymization. Our extensive empirical evaluation shows the trade-off among privacy protection, data distortion, delay and record reordering, and demonstrates the suitability of adapting video-compression techniques to anonymize database streams.

Publish/Subscribe systems like Twitter and Reddit let users communicate with many recipients without requiring prior personal connections. The content that participants of these systems publish and subscribe to is typically public, but they may nevertheless wish to remain anonymous. While many existing systems allow users to omit explicit identifiers, they do not address the obvious privacy risks of being associated with content that may contain a wide range of sensitive information. We present 2PPS (Twice-Private Publish-Subscribe), the first pub/sub protocol to deliver strong provable privacy protection for both publishers and subscribers, leveraging Distributed Point Function-based secret sharing for publishing and Private Information Retrieval for subscribing. 2PPS does not require trust in other clients and its privacy guarantees hold as long as even a single honest server participant remains. Furthermore, it is scalable and delivers latency suitable for microblogging applications. A prototype implementation of 2PPS can handle 100,000 concurrent active clients with 5 seconds end-to-end latency and significantly lower bandwidth requirements than comparable systems.

With continuous technological advancements, our homes become smarter by interconnecting more and more devices. Smart homes provide many advantages. However, they also introduce new privacy and security risks. Recent studies show that only a few people are aware of abstract risks, and most people are not aware of specific negative consequences. We developed a privacy and security awareness intervention for people who want to inform themselves about risks in the smart home context. Our intervention is based on research literature on risk perception and feedback from both lay users and security and privacy experts. We evaluated our intervention regarding its influence on participants' perceived threat, privacy attitude, motivation to avoid threats, willingness to pay, and time commitment to configure protective measures. The results of this evaluation show a significant increase for all these aspects. We also compared our intervention to information that users could obtain during an Internet search on the topic. In this comparison, our intervention evokes a significantly higher perceived threat and privacy attitude. It showed no significant difference for the other three scales. We discuss our findings in light of related work.

The change of pseudonym certificates for message authentication is the standard approach for privacy-friendly V2X communication. It's crucial to use an effective and robust pseudonym changing scheme as the location privacy of vehicles relies strongly on it. Therefore, in this work we analyzed a pseudonym change strategy that is recommended by the European C-ITS platform and has good chances to be included in a future European standard. By simulating a realistic urban traffic scenario within Luxembourg, applying and attacking the pseudonym change strategy, we could evaluate the effectiveness of the scheme. Overall, linking pseudonyms with simple traffic statistics in a realistic city traffic scenario is more challenging than related work suggests. However, the consideration of additional static information from the V2X communication, such as length and width of the vehicle, enormously improves the linking of pseudonyms, and thus enables tracking of vehicles and generation of motion profiles. The level of location privacy is thereby particularly influenced by the number of vehicles in the vicinity and especially their properties, as well as, of course, by the observation capabilities of the attacker. Our results suggest that the introduction of VANETs, even with the C-ITS pseudonym scheme, enables the tracking of vehicles, and thus will decrease location privacy in the future.

Efficiently integrity verification of received data requires Message Authentication Code (MAC) tags. However, while security calls for rather long tags, in many scenarios this contradicts other requirements. Examples are strict delay requirements (e.g., robot or drone control) or resource-scarce settings (e.g., LoRaWAN networks with limited battery capacity).

When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers’ privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data such that no observer can map between elements of the original sequence and the shuffled sequence with probability better than guessing. It allows for shuffling data encrypted under an additively homomorphic cryptosystem with constant round complexity and linear computational complexity. Being a general-purpose protocol, it is of relevance for a variety of practical use cases.

Through Information Reconciliation, two legitimate parties of Channel Reciprocity-based Key Generation assure that they extract the same key from local channel measurements. Current protocols exchange messages: Interactivity both causes delays and energy expenditure, and leaks information about the keying material to adversaries.We suggest non-interactive reconciliation, using a Siamese Network of CNNs that extracts reciprocal and suppresses nonreciprocal components in the measurements. Training and evaluating on real-world and synthetic data, we demonstrate that it blindly achieves higher correlation of the outputs at legitimate parties than the interactive state of the art, thus eliminating cost and information leakage at superior performance.

Cross domain tracking has become the rule, rather than the exception, and scripts that collect behavioral data from visitors across sites have become ubiquitous on the Web. The collections form comprehensive profiles of browsing patterns and contain personal, sensitive information. This data can easily be linked back to the tracked individuals, most of whom are likely unaware of this information's mere existence, let alone its perpetual storage and processing. As public pressure has increased, tracking companies like Google, Facebook, or Baidu now claim to anonymize their datasets, thus limiting or eliminating the possibility of linking it back to data subjects.In cooperation with Europe's largest audience measurement association we use access to a comprehensive tracking dataset to assess both identifiability and the possibility of convincingly anonymizing browsing data. Our results show that anonymization through generalization does not sufficiently protect anonymity. Reducing unicity of browsing data to negligible levels would necessitate removal of all client and web domain information as well as click timings. In tangible adversary scenarios, supposedly anonymized datasets are highly vulnerable to dataset enrichment and shoulder surfing adversaries, with almost half of all browsing sessions being identified by just two observations. We conclude that while it may be possible to store single coarsened clicks anonymously, any collection of higher complexity will contain large amounts of pseudonymous data.

After several years of research on onion routing, Camenisch and Lysyanskaya, in an attempt at rigorous analysis, defined an ideal functionality in the universal composability model, together with properties that protocols have to meet to achieve provable security. A whole family of systems based their security proofs on this work. However, analyzing HORNET and Sphinx, two instances from this family, we show that this proof strategy is broken. We discover a previously unknown vulnerability that breaks anonymity completely, and explain a known one. Both should not exist if privacy is proven correctly.In this work, we analyze and fix the proof strategy used for this family of systems. After proving the efficacy of the ideal functionality, we show how the original properties are flawed and suggest improved, effective properties in their place. Finally, we discover another common mistake in the proofs. We demonstrate how to avoid it by showing our improved properties for one protocol, thus partially fixing the family of provably secure onion routing protocols.

Greedy embeddings on rooted spanning trees are the most promising solution to provide sufficiently scalable routing in dynamic networks with restricted topologies, for instance friend-to-friend overlays such as the Dark Freenet and payment channel networks such as Lightning. Yet, they are not deployed in practice, as electing a root and configuring addresses remains an unsolved problem in adverse environments. Indeed, faulty or malicious nodes might provide incorrect coordinates, prevent the network from stabilizing by simulating dynamics, or not start the assignment of coordinates in their subtree at all. All of the above attacks may result in an inability to route. To mitigate the above attacks, we design a novel embedding algorithm with an adapted distance metric that only relies on interconnections between benign subtrees for successful delivery. In other words, even if roots of (sub-)trees are malicious or faulty, the remaining nodes still receive coordinates and can communicate with nodes in their tree branch as well as other branches reachable via the neighborhood of their benign ancestors. Extensive simulations demonstrate that we thus facilitate efficient routing even when seemingly decisive parts of the network are under adversarial control.

Cobalt is a key ingredient of lithium-ion batteries and therefore is crucial for many modern devices. To ensure ethical sourcing, consumers need a way to verify provenance of their cobalt-based products, including the percentage of artisanally mined (ASM) cobalt. Existing frameworks for provenance and supply chain traceability rely on distributed ledgers. Providing public verifiability via permissionless distributed ledgers is trivial. However, offering public verifiability based on confidential production details seems contradictory. Hence, existing frameworks lack public verifiability of ratios between commodities while ensuring confidentiality of supply chain details. We propose a protocol that allows end consumers to verify the percentage of ASM cobalt in their products. Unlike previous solutions, production details are published and processed entirely in encrypted form by employing homomorphic encryption and proxy re-encryption. Thus, it ensures a high level of confidentiality of supply chain data. It has constant consumer-side complexity, making it suitable for mobile devices.

In Physical Layer Security, knowing the reciprocal state information of the legitimate terminals' wireless channel is considered a shared secret. Although questioned in recent works, the basic assumption is that an eavesdropper, residing more than half of a wavelength away from the legitimate terminals, is unable to even obtain estimates that are correlated to the state information of the legitimate channel. In this work, we present a Machine Learning based attack that does not require knowledge about the environment or terminal positions, but is solely based on the eavesdropper's measurements. It still successfully infers the legitimate channel state information as represented in impulse responses. We show the effectiveness of our attack by evaluating it on two sets of real world ultra wideband channel impulse responses, for which our attack predictions can achieve higher correlations than even the measurements at the legitimate channel.

Responding to network security incidents requires interference with ongoing attacks to restore the security of services running on production systems. This approach prevents damage, but drastically impedes the collection of threat intelligence and the analysis of vulnerabilities, exploits, and attack strategies. We propose the live confinement of suspicious microservices into a sandbox network that allows to monitor and analyze ongoing attacks under quarantine and that retains an image of the vulnerable and open production network. A successful sandboxing requires that it happens completely transparent to and cannot be detected by an attacker. Therefore, we introduce a novel metric to measure the Quality of Deception (QoD) and use it to evaluate three proposed network deception mechanisms. Our evaluation results indicate that in our evaluation scenario in best case, an optimal QoD is achieved. In worst case, only a small downtime of approx. 3s per microservice (MS) occurs and thus a momentary drop in QoD to 70.26% before it converges back to optimum as the quarantined services are restored.

Implementing DASH, the most popular method for multimedia streaming, over NDN, a potential future Internet architecture, can substantially increase the network bandwidth utilization. However, inherent features of NDN can create new security risks for adaptive multimedia streaming. We propose a novel attack called Bitrate Oscillation Attack (BOA), which adversely exploits NDN's autonomous on-path caching and interest aggregation to unsettle DASH functionality. BOA forces the resolution and quality of video received by the attacked client to oscillate with high frequency and amplitude. Subsequently, we present CoMon-DAS, a framework for lightweight coordination that mitigates BOA and other attacks in NDN. Through extensive simulations, we demonstrate that BOA is very harmful for DAS over NDN, but can be significantly mitigated by CoMon-DAS.

Local, blind synchronization of Channel Impulse Responses for key generation algorithms without leaking details to adversaries is challenging, due to noise from transmission and measurements. Extracting the channel characteristics as a common source of randomness using CIR promises notable improvements in key bit rates for Physical Layer Security, however, reciprocity requirements necessitate successful synchronization at both ends. We propose a one-dimensional Gaussian filter towards this end, which is simple, more robust, and outperforms competing approaches in almost all settings. To evaluate the quality of our approach, we assess the synchronization accuracy on extensive datasets. Experiments using data generated synthetically with the standardized IEEE 802.15 UWB channel model indicate a performance improvement of up to 31%. Performance on par or better than all alternatives in real measurements underlines its superior robustness against synchronization errors.

Nodes in route-restricted overlays have an immutable set of neighbors, explicitly specified by their users. Popular examples include payment networks such as the Lightning network as well as social overlays such as the Dark Freenet. Routing algorithms are central to such overlays as they enable communication between nodes that are not directly connected. Recent results show that algorithms based on spanning trees are the most promising provably efficient choice. However, all suggested solutions fail to address how distributed spanning tree algorithms can deal with active denial of service attacks by malicious nodes. In this work, we design a novel self-stabilizing spanning tree construction algorithm that utilizes cryptographic signatures and prove that it reduces the set of nodes affected by active attacks. Our simulations substantiate this theoretical result with concrete values based on real-world data sets. In particular, our results indicate that our algorithm reduces the number of affected nodes by up to 74% compared to state-of-the-art attack-resistant spanning tree constructions.

Using V2X communication in platoons promises benefits regarding energy efficiency and fleet management. It is also a safety critical process with the potential to cause dangers to life and limb which needs to be secured against attackers. We propose two protocols for secure platoon communication and provide a comparative analysis of those protocols.

With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management.

Abstract Many anonymous communication networks (ACNs) with different privacy goals have been developed. Still, there are no accepted formal definitions of privacy goals, and ACNs often define their goals ad hoc. However, the formal definition of privacy goals benefits the understanding and comparison of different flavors of privacy and, as a result, the improvement of ACNs. In this paper, we work towards defining and comparing privacy goals by formalizing them as privacy notions and identifying their building blocks. For any pair of notions we prove whether one is strictly stronger, and, if so, which. Hence, we are able to present a complete hierarchy. Using this rigorous comparison between notions, we revise inconsistencies between the existing works and improve the understanding of privacy goals.

A distributed join is a fundamental operation for processing massive datasets in parallel. Unfortunately, computing an equi-join over such datasets is very resource-intensive, even when done in parallel. Given this cost, the equi-join operator becomes a natural candidate for optimization using approximation techniques, which allow users to trade accuracy for latency. Finding the right approximation technique for joins, however, is a challenging task. Sampling, in particular, cannot be directly used in joins; naïvely performing a join over a sample of the dataset will not preserve statistical properties of the query result.

The privacy of users in wireless mesh networks is threatened by attackers that trace the flow of packets through the network or trace users' movements within the network. Untraceable Linear Network Coding, a scheme to provide flow and movement untraceability, suggests to recode messages based on a specified matrix. However, the advantage an attacker gains by knowing this encoding strategy and the routing mechanism is not discussed. Hence, although claimed otherwise packet flows and movement of users might be traceable when the Untraceable Linear Network Coding Scheme is used. In this paper, we investigate this open point and introduce an attack based on the knowledge of the coding scheme and routing. We show the success of our polynomial time attack in a simulation: For most realistic parameters the attacker can trace flows in the network to the receiver's mesh node with a success rate of 80% or more. Additionally, we discuss countermeasures to reinforce the protection of the network coding scheme.

The advent of hardware Trojans is posing an increasing threat on complex integrated circuits. Network-on-Chip, the established communication infrastructure for many core systems-on-chip, are growing in complexity. Integration of third-party components, which are increasingly becoming valuable targets, exposes the surface for attacks through the injection of hardware Trojans. In this paper, we address active attacks on NoCs, and focus on the integrity of transmitted data. Basically, we use network coding for the transmission of data in order to increase efficiency and robustness.

Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.

Quantization, and the fact that channel characteristics are independent and identically distributed so far have received only little attention in reports about actual implementations of physical layer key generation schemes. They are merely assumed for channel reciprocity based key generation, although the secret key generation significantly relies on them. We set out to design a quantization preprocessing as well as an online quantization scheme which favours i.i.d. and uniform distribution of the generated values to achieve high entropy and key rates, and calculate the resulting mutual information between communication partners in a large, realistic measurement study. Our experiments indicate a remarkable increase in mutual information, and underline the applicability to various quantization and key generation schemes.

Increasing the traffic visibility, by monitoring network flow packets, provides valuable information for various network management tasks. The mirroring mode of flow packet monitoring requires the switches and routers to duplicate packets of interest, and to send them to flow monitors for in-depth analysis. A common practice to avoid the interference between the original and the mirrored flows is to transmit them separately, in two different planes (data plane and monitoring plane, respectively). In this paper, we aim at reducing the overall cost of transmitting both the original and mirrored flows. Towards that end, we present a generic monitoring framework called REMO. The key idea of REMO is twofold: (i) placing the flow monitors in central locations, and (ii) passing the original flows through the vicinity of the monitors. By doing so, REMO reduces the resources consumed in the monitoring plane, without unworthily increasing the resource consumption in the data plane. The results of extensive numerical simulations show that REMO effectively reduces the overall transmission cost, remarkably outperforming several baseline strategies, particularly when the transmission is more expensive in the monitoring plane.

This demo paper presents EConVi, a framework to support the implementation and visualization of the energy efficient software defined networking (SDN). This framework monitors the network workload, and dynamically changes the operational states of the switches and routing paths of flows, based on the controlling algorithms. The network topology, traffic workloads on each network link and switch, and the operational states of the switches are visualized. EConVi is able to manage the large-scaled networks simulated in the SDN emulators such as Mininet, or interact with DVFS-enabled hardware nodes installed with software switches, as shown in our demo.

P2P-based social networking services are severely challenged by churn and the lack of reliable service providers, especially considering the high frequency of posts and profile updates of their users. Improved consistency and data availability shall facilitate better acceptance, which in turn will enhance privacy, an inherent benefit of this class of systems. We present Lilliput, a P2P storage primitive designed with the characteristics of Online Social Network workloads in mind. Lilliput separates the storage of static bulk data (videos and photo albums) from the essential social glue (e.g. basic profile information, frequent updates, notifications, and personal messages): it provides the latter through agile, lightweight replica groups. Extensive simulations show that Lilliput ensures high data availability (99.07% to 99.64%) and consistency, with a small bandwidth usage under realistic usage and load models.

Printer identification is an important part of printer forensics to protect the reliability of printed documents or to track criminals. While the results of existing identification methods are promising, the robustness of these methods is usually not considered. Therefore, we analyzed an identification scheme which uses the halftone texture of color laser image prints as an intrinsic signature. We evaluated the signature's stability and its vulnerability to forgery. Our results show that the halftone textures depend on different driver settings and can be manipulated. Additionally we show that for specific forensical purposes the identification scheme can be applied to colored text as well.

Balancing the load in content addressing schemes for route-restricted networks represents a challenge with a wide range of applications. Solutions based on greedy embeddings maintain minimal state information and enable efficient routing, but any such solutions currently result in either imbalanced content addressing, overloading individual nodes, or are unable to efficiently account for network dynamics. In this work, we propose a greedy embedding in combination with a content addressing scheme that provides balanced content addressing while at the same time enabling efficient stabilization in the presence of network dynamics. We point out the tradeoff between stabilization complexity and maximal permitted imbalance when deriving upper bounds on both metrics for two variants of the proposed algorithms. Furthermore, we substantiate these bounds through a simulation study based on both real-world and synthetic data.

Defending against cache pollution attacks, highly detrimental attacks that are easy to implement in Named-Data Networking (NDN), currently suffers from the lack of coordination. Solving cache pollution attacks is a prerequisite for the deployment of NDN, which is widely considered to be the basis for the future Internet. We present CoMon++ to this end, a framework for lightweight coordination that protects from cache pollution and further attacks in NDN. Our simulation studies demonstrate that CoMon++ efficiently and effectively prevents cache pollution, remarkably outperforming a very notable state-of-the-art solution.

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing --- based on the chosen sample size --- can make a systematic trade-off between the output accuracy and computation efficiency.

Friend-to-friend (F2F) overlays, which restrict direct communication to mutually trusted parties, are a promising substrate for privacy-preserving communication due to their inherent membership-concealment and Sybil-resistance. Yet, existing F2F overlays suffer from a low performance, are vulnerable to denial-of-service attacks, or fail to provide anonymity. In particular, greedy embeddings allow highly efficient communication in arbitrary connectivity-restricted overlays but require communicating parties to reveal their identity. In this paper, we present a privacy-preserving routing scheme for greedy embeddings based on anonymous return addresses rather than identifying node coordinates. We show that the return addresses allow plausible deniability. Furthermore, we enhance the routing's resilience by using multiple embeddings and propose a method for efficient content addressing. Our extensive simulation study on real-world data indicates that our approach is highly efficient and effectively mitigates failures as well as powerful denial-of-service attacks.

Named-Data Networking (NDN) is a promising architecture for the future Internet. However, it is hampered by interest flooding, an NDN-tailored DDoS attack which has been shown to cause dropping majority of legitimate packets. While several defence mechanisms have been suggested against it, they cannot protect NDN against the Collusive Interest Flooding Attack (CIFA), a previously disregarded version of interest flooding. In CIFA, malicious clients issue interest packets that can be satisfied only by a malicious server. The server, in turn, responds with data packets just before expiration of the corresponding PIT entries. We study the effect of CIFA. Extensively simulating CIFA, we show that it affects the network and legitimate users almost as badly as an extensively researched version of interest flooding. Subsequently, we develop a generic defence mechanism against interest flooding attacks. The mechanism is based on CoMon, our framework for coordination in NDN. Thanks to CoMon, the attacks are detected and mitigated at an early stage by only a few routers. Via realistic simulations, we show that our defence decreases the amount of dropped legitimate packets remarkably, incurring a very low signalling overhead.

In pull-based Peer-to-Peer video streaming systems, peers exchange buffer maps to reveal the availability of video chunks in their buffer. When collecting these buffer maps, a malicious party can infer the system's overlay structure and even identify head nodes, the direct communication partners of the stream's source. Attacking these head nodes can isolate peers from the source resulting in a disruption of the video dissemination for most peers in the system. We introduce a lightweight SWAP scheme, which allows peers to proactively change their partners, to reduce the chance of head nodes to be identified by such an inference attacker. Extensive simulation studies demonstrate that our scheme effectively undermines the attack's accuracy in identifying head nodes. So, SWAP lowers the chunk miss ratio while causing only a slight increase in signaling overhead.

Huge quantities of videos are shared via Online Social Networks (OSN) like Facebook and are watched on mobile devices. Internet connections via cellular networks (UMTS / LTE) require the scarce resources radio bandwidth and battery power. Prefetching of videos in areas of WLAN availability has the potential to reduce the power consumption in comparison to data transmission via cellular networks and prefetching can help to avoid users running into traffic caps of their network providers. Furthermore, startup delays can be reduced. Social networks offer contextual information such as likes and comments as well as social graph information which can potentially be used to predict which content will be consumed in the near future. In this paper, we elaborate possibilities to predict content consumption based on the number of likes, comments and the social graph distance. Our detailed analysis of the media access patterns of more than 700 users in Facebook shows that the media consumption does not solely depend on the number of likes or comments. Users tend to watch videos that are uploaded by close friends and family members. Furthermore, the time a video preview stays in the browser-viewport before being clicked (pre-click delay) can be exploited to decrease startup delays.

The autonomous cache management in Content-Centric Networking (CCN) results in suboptimal caching decisions and implies cache-ignorant routing. Cache coordination and similar improvements hence have been the subject of several recent studies. The proposed solutions, however, are either impractical due to their massive coordination overhead, or of limited benefit since they cannot realize perfect coordination. We present CoMon, an architecture for network-wide coordinated caching. CoMon realizes an affordable, yet highly effective, coordination by assigning monitoring and cache-aware (re)routing tasks to only a few nodes, through which the majority of traffic is expected or enforced to pass. CoMon, by design, maximizes the diversity of cached contents and reduces cache replacements. In addition, our simulation study using ISP topologies, shows that CoMon under several scenarios, when coordinates through a small ratio of the nodes, reduces the server hit ratio (i.e. the ratio of requests consumed by the origin content providers) of both CCN and notable related work, remarkably.

Virtual overlays generate topologies for greedy routing, like rings or hypercubes, on connectivity restricted networks. They have been proposed to achieve efficient content discovery in the Darknet mode of Freenet, for instance, which provides a private and secure communication platform for dissidents and whistle-blowers. Virtual overlays create tunnels between nodes with neighboring addresses in the topology. The routing performance hence is directly related to the length of the tunnels, which have to be set up and maintained at the cost of communication overhead in the absence of an underlying routing protocol. In this paper, we show the impossibility to efficiently maintain sufficiently short tunnels. Specifically, we prove that in a dynamic network either the maintenance or the routing eventually exceeds polylog cost in the number of participants. Our simulations additionally show that the length of the tunnels increases fast if standard maintenance protocols are applied. Thus, we show that virtual overlays can only offer efficient routing at the price of high maintenance costs.

Hybrid Peer-to-Peer streaming systems combine the advantages of an efficient push-based with a more resilient pull-based system to deliver video streams over the Internet. In this manner, hybrid systems offer low latency and an increased robustness to failures and node churn. However, current hybrid systems is vulnerable to misbehaving nodes and deliberate attacks. By taking central positions in the overlay, malicious nodes can perform extremely harmful Denial-of-Service (DoS) attacks. We propose RBCS, a novel backbone construction scheme, that is highly resilient against DoS attacks while maintaining fast content dissemination. RBCS incorporates stable peers into a manipulation-resistant multi-tree backbone overlay, which is resilient against both attacks and node churn. Additionally, RBCS securely identifies stable peers by using only local knowledge about the participation time of others. Extensive simulations indicate that RBCS outperforms the state-of-the-art in being more resilient against attacks at the price of a slightly increased overhead.

Named-Data Networking (NDN) is a promising architecture for future Internet. Its design, however, can be misused to perform a new DDoS attack known as the Interest Flooding Attack (IFA). In IFA, the attacker issues non-satisfiable interest packets, aiming to drop legitimate interest packets by overwhelming pending interest tables in NDN routers. Prior defence mechanisms are not highly effective, harm legitimate interest packets, and/or incur high overhead. We propose a coordinated defence mechanism against IFAs. We realize our solution by adapting CoMon, a framework that we developed previously to coordinate caching-related decisions in NDN, motivated by its effective, yet affordable, coordination. In our solution, IFAs are detected and mitigated by few routers based on aggregated knowledge of traffic and forwarding states. These routers are selected by a novel heuristic enabling them to observe the entire traffic at an early stage. Extensive simulations confirm the feasibility and effectiveness of our solution.

Web of Trust (WoT) graphs represent trust relations between people. They are used for research and analysis in various domains. Real-world instances are only available in sizes of up to 55k vertices. This renders the analysis of larger systems based on realistic input graphs impossible. To close this gap, we develop a growth model to generate WoT graphs of arbitrary size. New edges are formed based on realistic assumptions about trust establishment. We analyze the growth of a real-world WoT and perform a parameter study of our model. We compare both with many existing models and show that ours is the only one that matches the properties of the real-world WoT.

Decentralized Online Social Networks (DOSN) seek to increase their users' privacy by removing centralized data storage and control. Their lack of usability and competitive features has severely hindered their success. Even a privacy preserving user discovery, which seems paramount for a social networking service, so far has not been implemented. Aiming at a hybrid architecture of decentralized servers, as it is suggested by the currently most successful DOSN Diaspora amongst others, we suggest a distributed discovery of users. Our discovery mechanism protects privacy of users and does not create vulnerabilities to mass collections of profiles or SPAM. The scheme adapts known techniques like DHT, indirection, and proof of knowledge, to meet the service specific requirements. A general implementation for a popular XMPP server furthermore gives proof of our concept and represents a first step towards constructing a reliable distributed social networking service with user discovery on decentralized servers, without leaking any information about its users.

The design of secure and usable access schemes to personal data represent a major challenge of online social networks (OSNs). State of the art requires prior interaction to grant access. Sharing with users who are not subscribed or previously have not been accepted as contacts in any case is only possible via public posts, which can easily be abused by automatic harvesting for user profiling, targeted spear-phishing, or spamming. Moreover, users are restricted to the access rules defined by the provider, which may be overly restrictive, cumbersome to define, or insufficiently fine-grained. We suggest a complementary approach that can be easily deployed in addition to existing access control schemes, does not require any interaction, and includes even public, unsubscribed users. It exploits the fact that different social circles of a user share different experiences and hence encrypts arbitrary posts. Assembling only well-established cryptographic primitives, we prove that the security of our scheme is determined by the entropy of the required knowledge. We consequently analyze the efficiency of an informed dictionary attack and assess the entropy to be on par with common passwords. A fully functional implementation is used for performance evaluations, and available for download on the Web.

The widely used distributed hash table (DHT) in KAD is commonly analyzed and optimized based on partial measurements and simulation results, which are limited in scope and subject to simplification. An accurate characterization, however, is vital for a thorough understanding and effective enhancement. Analyzing and comparing complete real graphs collected from a large-scale measurement campaign as well as synthetic graphs generated by a novel simulation model, we study their degree distributions as well as resilience in face of random departure and targeted attacks. Our results show that the online KAD graph, although scale-free, is highly robust not only to random departure, but also to targeted attacks, making it suitable for distributed applications requiring a high resilience. Resilience to random departure and shape of degree distribution are well modelled by the simulations. However, due to a greatly increased ratio of stale routing information, the complete graph in the real system is much more vulnerable to targeted attacks compared to estimations based on simulative results.

Discovery of nodes and content in large-scale distributed systems is generally based on Kademlia, today. Understanding Kademlia-type systems to improve their performance is essential for maintaining a high service quality for an increased number of participants, particularly when those systems are adopted by latency-sensitive applications. This paper contributes to the understanding of Kademlia by studying the impact of diversifying neighbours' identifiers within each routing table bucket on the lookup performance. We propose a new, yet backward-compatible, neighbour selection scheme that attempts to maximize the aforementioned diversity. The scheme does not cause additional overhead except negligible computations for comparing the diversity of identifiers. We present a theoretical model for the actual impact of the new scheme on the lookup's hop count and validate it against simulations of three exemplary Kademlia-type systems. We also measure the performance gain enabled by a partial deployment for the scheme in the real KAD system. The results confirm the superiority of the systems that incorporate our scheme.

With over 400,000 active users, Diaspora is the largest decentralized Online Social Network, today. Decentralization entails complications, especially for user discovery and meaningful authorization. Analysing the system, we identified two vulnerabilities and implemented exploits that could be used to first locate a large fraction of the users, and then delete their profiles including all information from the system entirely, without any privileges whatsoever. We show the feasibility of both attacks, and provide means for mitigation as well as general recommendations to avoid such vulnerabilities in decentralized systems in the future.

Routing in Darknets, membership concealing overlays for pseudonymous communication, like for instance Freenet, is insufficiently analyzed, barely understood, and highly inefficient. These systems at higher performance are promising privacy preserving solutions for social applications. This paper contributes a realistic analytical model and a novel routing algorithm with provable polylog expected routing length. Using the model, we additionally prove that this can not be achieved by Freenet's routing. Simulations support that our proposed algorithm achieves a better performance than Freenet for realistic network sizes.

Our main contribution in this work is a deployable multitree-push system for P2P-based live streaming. It runs on both desktop PCs and Android-based mobile devices. Additionally, it provides controlling, monitoring, and measurement functionalities which help with debugging in the development phase, visualize the topology during a demonstration, and support the deployment of test scenarios in a distributed setting. Besides, the generic architecture of the system also allows for the extension to other classes of streaming systems.

Motivated by the popularity of content distribution and file sharing applications that nowadays dominate Internet traffic, we focus on the incentive mechanism of a very popular, yet not very well studied, peer-to-peer application, eMule. In our work, we recognize that the incentive scheme of eMule is more sophisticated than current alternatives (e.g., BitTorrent) as it uses a general, priority-based, time-dependent queuing discipline to differentiate service among cooperative users and free-riders. In this paper, we describe a general model of such an incentive mechanism and analyze its properties in terms of application performance. We validate our model using both numerical simulations (when analytical techniques become prohibitive) and with a measurement campaign of the live eMule system. Our results, in addition to validating our model, indicate that the incentive scheme of eMule suffers from starvation. Therefore, we present an alternative scheme that mitigates this problem, and validate it through numerical simulations and a second measurement campaign.

Topology control for Wireless Sensor Networks (WSN) is a frequently tackled challenge, for which no satisfying general solution for realistic deployments has been found to the current day. Aiding to minimize unnecessary transmissions, it nevertheless represents a crucial function of WSN, in the light of their pursuit of efficiency. kTC is a new WSN topology control that unlike prior art neither relies on location information, nor on complex geometric structures, which could leave doubts about a practical feasibility. Even though location-free approaches have been proposed to circumvent systematic problems, they do not address issues like robustness and adaptability satisfyingly, which may lead to disconnection in real world deployments. kTC is a location-free approach that adapts topologies dynamically in face of changing environmental influences. It is based on a local, pattern-based heuristic, and transmitting only two messages per node to construct the topology it is highly scalable. The graphs kTC creates are symmetric, connected, and planar; they have bounded degree and nodes are θ-separated. Simulative evaluations indicate that kTC outperforms known topology control schemes. A preliminary deployment on a sensor testbed corroborates the obtained results and acts as proof of concept for kTC.

Energy is the most limiting factor in wireless sensor networks. Harvesting solar energy is a feasible solution to overcome the energy-constraint in some applications. It enables a theoretically infinite network lifetime, sustaining a mode of operation termed energy neutral consumption rate. The challenge arises, how can the harvested energy be utilized to maximize the performance of the sensor network. Considering a field monitoring application the performance is measured as the sustained sampling rate of the sensors. Maximizing the sampling rate needs to take the spatio-temporal distribution of load and energy into account, to prevent the over- loading of nodes. In [12] they introduced a optimal, theoretical solution based on perfect global knowledge. In this paper we propose the solar-aware distributed flow (SDF) approach. SDF enables each node to predict the harvested energy, calculate a sustainable flow and control its local neighborhood. Extensive simulations confirmed that SDF achieves over 80% of the theoretical optimum, while introducing negligible overhead. Keywords-Wireless Sensor Networks, Solar Harvesting, Energy Neutral Consumption Rate, Sampling Rate.

Decentralized approaches for online social networks (OSNs) have been of recent research interest, enabling users to create profiles and share data like in other OSNs as, e.g., Facebook. Since the decentralized architecture does not contain a central authority that is able perform access control, encryption is needed to ensure the confidentiality of published data. This paper outlines strict requirements and weak constraints for the encryption of data attributes in decentralized OSNs. Subsequently, an overview of possible cryptographic solutions is given and their suitability according to these requirements is analyzed. As a result, the differences and trade-offs between and within the given approaches are expounded. The outcome of this paper can be used as a foundation for further investigations on this topic.

Topology adaptation is a vital operation in technological networks. It is frequently implemented as either an external process or a distributed online optimization that relies on gathering knowledge on the overall state of the system. In this work we propose MBO, a novel approach that uses network motifs (a local, stochastic metric) for distributed topology optimization of arbitrary, adaptable networks. In order to give a proof of concept we chose to optimize structured Peer-to-Peer overlays towards a fair load balancing. MBO is parametrized using target motif signatures of networks, which are derived from exemplary, generated topologies with the desired properties - a fair load balancing in the demonstrated case. Extensive simulations indicate that for CAN and Kademlia, two different types of P2P systems, MBO leads to a well balanced load, while being minimally intrusive.

High robustness against churn and resilience towards adverse behavior are the key requirements for reliable Peer-to-Peer streaming systems. Their highly inter-dependent nature, based on the cooperative service delivery between all peers, necessitates a systematic and structural resilience, which, in their design of self-organization and decentralized control, is very challenging to assure. Reliable services, and hence a structural resilience, still are a vital prerequisite for any commercial deployment of P2P-based live streaming systems or IPTV infrastructures. We propose an entirely distributed, Motif-based topology optimization to this end. Concise comparisons show that it creates topologies almost as resilient as the current state of the art, yet causing significantly less, almost negligible overhead in both computation and messaging, and still offering even better protection of information on the overall system.

Social networking services (SNS), which provide the application with the most probably highest growth rates in the Internet today, raise serious security concerns, especially with respect to the privacy of their users. Multiple studies have shown the vulnerability of these services to breaches of privacy and to impersonation attacks mounted by third parties, however the centralized storage at the providers of SNS represents an additional quite significant weakness that so far has not satisfyingly been addressed. In this paper we show the feasibility of ldquoSafebookrdquo, our proposal for the provision of a competitive social networking service, which solves these vulnerabilities by its decentralized design, leveraging on the real life relationships of its users and means of cryptography.

Social networking sites have been increasingly gaining popularity. Well-known sites such as Facebook have been reporting growth rates as high as 3% per week. Many social networking sites have millions of registered users who use these sites to share photographs, contact long-lost friends, establish new business contacts and to keep in touch. In this paper, we investigate how easy it would be for a potential attacker to launch automated crawling and identity theft attacks against a number of popular social networking sites in order to gain access to a large volume of personal user information. The first attack we present is the automated identity theft of existing user profiles and sending of friend requests to the contacts of the cloned victim. The hope, from the attacker's point of view, is that the contacted users simply trust and accept the friend request. By establishing a friendship relationship with the contacts of a victim, the attacker is able to access the sensitive personal information provided by them. In the second, more advanced attack we present, we show that it is effective and feasible to launch an automated, cross-site profile cloning attack. In this attack, we are able to automatically create a forged profile in a network where the victim is not registered yet and contact the victim's friends who are registered on both networks. Our experimental results with real users show that the automated attacks we present are effective and feasible in practice.

Using a peer-to-peer approach for live multimedia streaming applications offers the promise to obtain a highly scalable, decentralized, and robust distribution service. When constructing streaming topologies, however, specific care has to be taken in order to ensure that quality of service requirements in terms of delay, jitter, packet loss, and stability against deliberate denial of service attacks are met. In this paper, we concentrate on the latter requirement of stability against denial-of-service attacks. We present an analytical model to assess the stability of overlay streaming topologies and describe attack strategies. Building on this, we describe topologies, which are optimally stable toward perfect attacks based on global knowledge, and give a mathematical proof of their optimality. The formal construction and analysis of these topologies using global knowledge lead us to strategies for distributed procedures, which are able to construct resilient topologies in scenarios, where global knowledge can not be gathered. Experimental results show that the topologies created in such a real-world scenario are close to optimally stable toward perfect denial of service attacks.

Confidential communication of live-generated multimedia data distributed via application level multicast (ALM) still remains a mostly unaddressed subject even though some important usage scenarios, e.g. paid subscription services or personal video-streaming, are anticipated to gain more widespread use as the Internet continues to evolve into the common transport platform for all kinds of services. In this article, we examine the specific requirements for key management schemes to be used in ALM-based distribution systems and analyze existing key management approaches with respect to these requirements [1, 2, 3]. Based on the results of this analysis, we design a new key management scheme that combines ideas of the Logical Key Hierarchy (LKH) protocol [4, 5] and the Iolus approach [6]. We compare the resulting scheme to a simple approach that is based on pairwise keys between neighboring nodes without further key-hierarchy based optimization and that serves as a benchmark. Our results of a comparative simulation study clearly indicate the suitability of our scheme for ALM-based livestreaming.

In this paper, we present Bandwidth Class Based Streaming (BCBS), an application layer multicast for multimedia services. BCBS focuses on multi source live streaming, and following a locality model based on round trip times, it creates network efficient streaming meshes. The load balancing selects multiple nodes as streaming sources and is organised subscription based instead of request based. We describe a simulation study of the load balancing and tree construction procedures. The results show that BCBS creates network efficient overlays with respect to stretch and link stress.