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Institute
Internet voting systems are supposed to meet the same high stan-
dards as traditional paper-based systems when used in real political
elections: freedom of choice, universal and equal suffrage, secrecy
of the ballot, and independent verifiability of the election result.
Although numerous Internet voting systems have been proposed
to achieve these challenging goals simultaneously, few come close
in reality.
We propose a novel publicly verifiable and practically efficient
Internet voting system, DeVoS, that advances the state of the art.
The main feature of DeVoS is its ability to protect voters’ freedom
of choice in several dimensions. First, voters in DeVoS can intu-
itively update their votes in a way that is deniable to observers but
verifiable by the voters; in this way voters can secretly overwrite
potentially coerced votes. Second, in addition to (basic) vote privacy,
DeVoS also guarantees strong participation privacy by end-to-end
hiding which voters have submitted ballots and which have not.
Finally, DeVoS is fully compatible with Perfectly Private Audit Trail,
a state-of-the-art Internet voting protocol with practical everlasting
privacy. In combination, DeVoS offers a new way to secure free
Internet elections with strong and long-term privacy properties.
Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike.
Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
(2023)
Tor is the most popular anonymization network with millions of daily users. This makes it an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods preferring Tor nodes with higher resilience to active BGP attacks.
In this work, we analyze the implications of such proposals and demonstrate that two state-of-the-art path selection methods, namely Counter-RAPTOR and DPSelect, are not as secure as thought before. First, we show that DPSelect achieves only one third of its originally claimed resilience and, thus, is not as resilient as widely accepted. Second, we reveal that the resilience to active BGP attacks on the way back, i.e., from the first anonymization node to a given Tor user, provided by both methods is significantly lower than on the forward path. Beside their lower resilience in specific cases, we show that for particular users the usage of Counter-RAPTOR and DPSelect also leads to leakage of user’s location. Furthermore, we uncover the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious Tor middle node can fingerprint the user’s location and the confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.
The overwhelmingly widespread use of Internet of Things (IoT) in different application domains brought not only benefits, but, alas, security concerns as a result of the increased attack surface and vectors. One of the most critical mechanisms in IoT infrastructure is key management. This paper reflects on the problems and challenges of existing key management systems, starting with the discussion of a recent real-world attack. We identify and elaborate on the drawbacks of security primitives based purely on physical variations and – after highlighting the problems of such systems – continue on to deduce an effective and cost-efficient key management solution for IoT systems extending the symbiotic security approach in a previous work. The symbiotic architecture combines software, firmware, and hardware resources for secure IoT while avoiding the traditional scheme of static key storage and generating entropy for key material on-the-fly via a combination of a Physical Unclonable Func tion (PUF) and pseudo-random bits pre-populated in firmware.
Vote privacy is a fundamental right, which needs to be protected not only during an election, or for a limited time afterwards, but for the foreseeable future. Numerous electronic voting (e-voting) protocols have been proposed to address this challenge, striving for everlasting privacy. This property guarantees that even computationally unbounded adversaries cannot break privacy of past elections. The broad interest in secure e-voting with everlasting privacy has spawned a large variety of protocols over the last three decades. These protocols differ in many aspects, in particular the precise security properties they aim for, the threat scenarios they consider, and the privacy-preserving techniques they employ. Unfortunately, these differences are often opaque, making analysis and comparison cumbersome. In order to overcome this non-transparent state of affairs, we systematically analyze all e-voting protocols designed to provide everlasting privacy. First, we illustrate the relations and dependencies between all these different protocols. Next, we analyze in depth which protocols do provide secure and efficient approaches to e-voting with everlasting privacy under realistic assumptions, and which ones do not. Eventually, based on our extensive and detailed treatment, we identify which research problems in this field have already been solved, and which ones are still open. Altogether, our work offers a well - founded reference point for conducting research on secure e - voting with everlasting privacy as well as for future - proofing privacy in real - world electronic elections.
Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls
(2023)
During the first days of the 2022 Russian invasion of Ukraine, Russia’s media regulator blocked access to many global social media
platforms and news sites, including Twitter, Facebook, and the
BBC. To bypass the information controls set by Russian authorities,
pro-Ukrainian groups explored unconventional ways to reach out
to the Russian population, such as posting war-related content in
the user reviews of Russian business available on Google Maps or
Tripadvisor. This paper provides a first analysis of this new phenomenon by analyzing the creative strategies to avoid state censorship.
Specifically, we analyze reviews posted on these platforms from
the beginning of the conflict to September 2022. We measure the
channeling of war messages through user reviews in Tripadvisor
and Google Maps, as well as in VK, a popular Russian social network.
Our analysis of the content posted on these services reveals
that users leveraged these platforms to seek and exchange humanitarian
and travel advice, but also to disseminate disinformation and
polarized messages. Finally, we analyze the response of platforms
in terms of content moderation and their impact.
Controller Area Network (CAN) is a masterless serial bus designed and widely used for the exchange of mission and time-critical information within commercial vehicles. In-vehicle communication is based on messages sent and received by Electronic Control Units (ECUs) connected to this serial bus network. Although unencrypted, CAN messages are not easy to interpret. In fact, Original Equipment Manufacturers (OEMs) attempt to achieve security through obscurity by encoding the data in their proprietary format, which is kept secret from the general public. As a result, the only way to obtain clear data is to reverse engineer CAN messages. Driven by the need for in-vehicle message interpretation, which is highly valuable in the automotive industry, researchers and companies have been working to make this process automated, fast, and standardized. In this paper, we provide a comprehensive review of the state of the art and summarize the major advances in CAN bus reverse engineering. We are the first to provide a taxonomy of CAN tokenization and translation techniques. Based on the reviewed literature, we highlight an important issue: the lack of a public and standardized dataset for the quantitative evaluation of translation algorithms. In response, we define a complete set of requirements for standardizing the data collection process. We also investigate the risks associated with the automation of CAN reverse engineering, in particular with respect to the security network and the safety and privacy of drivers and passengers. Finally, we discuss future research directions in CAN reverse engineering.
This paper presents a tool to Explore Process Discovery (EPD) results using activity projection. Our EPD-Tool aims at exploring quality changes after removing activities from an event log. The main idea is to create a projected event log for every non-empty subset of activities and apply process discovery and conformance checking on them. The tool has been implemented as a plugin in ProM. First, EPD-Tool uses a process discovery algorithm to discover Petri net models for each projected event log. Then, EPD-Tool uses a conformance checking technique to compute conformance measures for each projected event log and model pair (L, N), e.g., fitness, precision, and F1-score. Finally, a dendrogram is generated to visualize the relationship between each log-model pair, thus enabling the systematic exploration of the different models using the dendrogram to find the best-performing node, i.e., a best log-model pair. This method prioritizes activities and detects redundancy in the process, which contributes to process enhancement. Conversely, critical activities are uncovered to help to shorten the processing time or save the process cost. This paper presents the EPD-Tool implementation and some example results.
Recently, several robust principal component analysis (RPCA) models were presented to enhance the robustness of PCA by exploiting the robust norms as their loss functions. But an important problem is that they have no ability to discriminate outliers from correct samples. To solve this problem, we propose a robust principal component analysis based on discriminant information (RPCA-DI). RPCA-DI disentangles the robust PCA with a two-step fashion: the identification and the processing of outliers. To identity outliers, a sample representation model based on entropy regularization is constructed to analyze the membership of data belonging to the principal component space(PC) and its orthogonal complement(OC), the discriminative information of data will be extracted based on measuring the differences of retained information on PC(or OC) of data. By this way, we can discriminate correct samples when we deal with outliers, which is more reasonable for robustness learning respective to previous works. In the noise processing step, in addition to considering the levels of noise, the resistance of the sample points to noise is also considered to prevent overfitting, thereby improving the generalization performance of RPCA-DI. Finally, an iterative algorithm is designed to solve the corresponding model. Compared with some state-of-art RPCA methods on artificial datasets, UCI datasets and face databases that verifies the effectiveness of our proposed algorithm.
The increasing demand for secure and anonymous transactions raises the popularity of ring signatures, which is a digital signature scheme that allows identifying a group of possible signers without revealing the identity of the actual signer. This paper presents efficient supersingular isogeny-based ring signature and linkable ring signature schemes that will find potential applications in post-quantum technologies. We develop the ring signature scheme by applying the Fiat-Shamir transform on the sigma protocol for a ring which we obtain from the supersingular isogeny-based interactive zero-knowledge identification scheme by adopting the scheme for a ring. We also extend our ring signature protocol with an additional parameter, i.e., a tag that provides to detect if a signer issues two signatures concerning the same ring by preserving anonymity and linkable anonymity. The signature size of our ring signature protocols increases logarithmically in the size of the ring thanks to the Merkle trees. We show the security proofs and efficiency analyses of the protocols offered. Moreover, we provide the implementation results of the supersingular isogeny-based ring signature, which offers small signature sizes for NIST post-quantum security levels.
In the last decades, the increasing complexity of industrial information technology has led to the emergence of new trends in manufacturing. Factories are using multiple Internet of Things (IoT) platforms to harvest sensor information to improve production. Such a transformation contributes to efficiency growth and reduced production costs. To deal with the heterogeneity of the services within an IoT system, Service-Oriented Architecture (SOA) is referred to in the literature as being advantageous for the design and development of software to support IoT-based production processes. The aim of SOA-based design is to provide the leverage to use and reuse loosely coupled IoT services at the middleware layer to minimise system integration problems. We propose a system architecture that follows the SOA architectural pattern and enables developers and business process designers to dynamically add, query or use instances of existing modular software in the IoT context. Furthermore, an analysis of utilization of modular software that presents some challenges and limitations of this approach is also in the scope of this work.
Zero-defect manufacturing terminology standardization: Definition, improvement, and harmonization
(2022)
Zero-Defect Manufacturing (ZDM) is the next evolutionary step in quality management for manufacturing that makes use of Industry 4.0 technologies to support quality in manufacturing. These technologies help reduce the cost of inspection, allowing for more inspection points throughout the manufacturing process, reducing the size of quality feedback loops, and guaranteeing that no defective product is delivered to the customer. There are several ZDM-related initiatives, but still no harmonized terminology. This article describes the methodological approach to provide a common agreement on the ZDM concept and its associated terminology taking place within an open CEN-CENELEC Workshop. The methodology has the support of ISO standards for terminology work such as ISO 704, ISO 860, and ISO 10241–1/2. This work shows that the terminology for ZDM has a significant overlap with the terminology of quality management, metrology, dependability, statistics, non-destructive inspection, and condition monitoring. The proposed new terms and definitions can be used to further extend ISO’s and IEC’s already available terminologies and support present and future researchers in the field to conduct their research using a common vocabulary.
Attack detection in enterprise networks is increasingly faced with large data volumes, in part high data bursts, and heavily fluctuating data flows that often cause arbitrary discarding of data packets in overload situations which can be used by attackers to hide attack activities. Attack detection systems usually configure a comprehensive set of signatures for known vulnerabilities in different operating systems, protocols, and applications. Many of these signatures, however, are not relevant in each context, since certain vulnerabilities have already been eliminated, or the vulnerable applications or operating system versions, respectively, are not installed on the involved systems. In this paper, we present an approach for clustering data flows to assign them to dedicated analysis units that contain only signature sets relevant for the analysis of these flows. We discuss the performance of this clustering and show how it can be used in practice to improve the efficiency of an analysis pipeline.
The emergence of distributed digital currencies has raised the need for a reliable consensus mechanism. In proof-of-stake cryptocurrencies, the participants periodically choose a closed set of validators, who can vote and append transactions to the blockchain. Each validator can become a leader with the probability proportional to its stake. Keeping the leader private yet unique until it publishes a new block can significantly reduce the attack vector of an adversary and improve the throughput of the network. The problem of Single Secret Leader Election (SSLE) was first formally defined by Boneh et al. in 2020.
In this work, we propose a novel framework for constructing SSLE protocols, which relies on secure multi-party computation (MPC) and satisfies the desired security properties. Our framework does not use any shuffle or sort operations and has a computational cost for N parties as low as O(N) of basic MPC operations per party. We improve the stateof-the-art for SSLE protocols that do not assume a trusted setup. Moreover, our SSLE scheme efficiently handles weighted elections. That is, for a total weight S of N parties, the associated costs are only increased by a factor of log S. When the MPC layer is instantiated with techniques based on Shamir’s secret-sharing, our SSLE has a communication cost of O(N2) which is spread over O(logN) rounds, can tolerate up to t < N/2 of faulty nodes without restarting the protocol, and its security relies on DDH in the random oracle model. When the MPC layer is instantiated with more efficient techniques based on garbled circuits, our SSLE requires all parties to participate, up to N −1 of which can be malicious, and its security is based on the random oracle model.
Anonymous communication on the Internet is about hiding the relationship between communicating parties. At NDSS ’16, we presented a new website fingerprinting approach, CUMUL, that utilizes novel features and a simple yet powerful algorithm to attack anonymization networks such as Tor. Based on pattern observation of data flows, this attack aims at identifying the content of encrypted and anonymized connections. Apart from the feature generation and the used classifier, we also provided a large dataset to the research community to study the attack at Internet scale.
In this paper, we emphasize the impact of our artifacts by analyzing publications referring to our work with respect to the dataset, feature extraction method, and source code of the implementation. Based on this data, we draw conclusions about the impact of our artifacts on the research field and discuss their influence on related cybersecurity topics. Overall, from 393 unique citations, we discover more than 130 academic references that utilize our artifacts, 61 among them are highly influential (according to SemanticScholar), and at least 43 are from top-ranked security venues.
This data underlines the significant relevance and impact of our
work as well as of our artifacts in the community and beyond.
Node discovery is a fundamental service for any overlay network, including anonymization networks. Although anonymization and node discovery are two disjoint services, the node discovery has a direct impact on the anonymization. Centralized methods require a trusted third party, limit the network scalability, and are vulnerable to intersection (statistical disclosure) attacks. Therefore, several distributed node discovery methods were proposed to meet the security requirements of anonymization networks through additional structures within Distributed Hash Tables (DHTs). However, they require a high management overhead, a strict cooperation between nodes, and are susceptible to active and passive attacks.We propose WhisperChord—an alternative distributed node discovery approach, which incorporates gossiping into structured overlays. WhisperChord is based on a Chord DHT and neither creates any additional structures within the DHT nor requires any trusted third party. Via simulations, we show that our method provides superior protection against active attacks than prior methods and can effectively thwart information leakages.
Node discovery is a fundamental service for any overlay network. It is a particular challenge to provide unbiased discovery in untrustworthy environments, e.g., anonymization networks. Although a major line of research focused on solving this problem, proposed methods have been shown to be vulnerable either to active attacks or to leak routing information, both threatening the anonymity of users. In response, we propose GuardedGossip—a novel gossip-based node discovery protocol—that achieves an unbiased random node discovery in a fully-decentralized and highly-scalable fashion. It is built on top of a Chord distributed hash table (DHT) and relies on witness nodes and bound checks to resist active attacks. To limit routing information leakages, GuardedGossip uses gossiping to create uncertainty in the process of node discovery. By incorporating the principles of DHTs with the unstructured nature of gossiping in a subtle way, we profit from the strengths of both techniques while carefully mitigating their shortcomings. We show that GuardedGossip provides a sufficient level of security for users even if 20% of the participating nodes are malicious. Concurrently, our system scales gracefully and provides an adequate overhead for its security and privacy benefits.
Website fingerprinting (WFP) attacks on the anonymity network Tor have become ever more effective. Furthermore, research discovered that proposed defenses are insufficient or cause high overhead. In previous work, we presented a new WFP defense for Tor that incorporates multipath transmissions to repel malicious Tor nodes from conducting WFP attacks. In this demo, we showcase the operation of our traffic splitting defense by visually illustrating the underlying Tor multipath transmission using LED-equipped Raspberry Pis.
Trustless, Censorship-Resilient and Scalable Votings in the Permission-Based Blockchain Model
(2021)
Voting systems are the tool of choice when it comes to settle an agreement of different opinions. We propose a solution for a trustless, censorship-resilient and scalable electronic voting platform. By leveraging the blockchain together with the functional encryption paradigm, we fully decentralize the system and reduce the risks that a voting provider, like a corrupt government, does censor or manipulate the outcome.
To obtain a compact and effective low-dimensional representation, recently, most existing discriminant manifold learning methods have integrated manifold learning into discriminant analysis (DA) for extracting the intrinsic structure of data. These methods learn two kinds of adjacency graphs, such as intrinsic graph and penalty graph, to characterize the similarity between samples from intraclass and the pseudo similarity of interclass. However, they treat every sample equally, which results in the following defects: (1) These methods cannot accurately characterize the marginal region among different classes only through penalty graphs. (2) They can not identify the noisy and outlier samples which reduce the robustness of these methods. To address these problems, we introduce an adaptive adjacency factor to perform the discriminative based reliability analysis for each sample. By integrating the adjacency factor into discriminant manifold learning methods, we propose a novel method for DA namely discriminant analysis based on reliability of local neighborhood (DA-RoLN). We mainly have three contributions in this paper: (1) By the introduction of adjacency factor, sample points can be divided into three parts: intraclass samples, marginal samples, and outliers. Therefore, DA-RoLN emphasizes the effect of valid samples and filters the influence of outliers. (2) We adaptively calculate the adjacency factor in low-dimensional space, thus, the margin between different classes in low-dimensional space is emphasized. (3) An iterative algorithm is developed to solve the objective function of DA-RoLN, and it is easy to solve with a low computational cost. Extensive experimental results show the effectiveness of DA-RoLN.
Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms.
Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
(2020)
Website fingerprinting (WFP) aims to infer information about the
content of encrypted and anonymized connections by observing
patterns of data flows based on the size and direction of packets. By
collecting traffic traces at a malicious Tor entry node — one of the
weakest adversaries in the attacker model of Tor — a passive eavesdropper can leverage the captured meta-data to reveal the websites visited by a Tor user. As recently shown, WFP is significantly more effective and realistic than assumed. Concurrently, former WFP defenses are either infeasible for deployment in real-world settings or defend against specific WFP attacks only.
To limit the exposure of Tor users to WFP, we propose novel
lightweight WFP defenses, TrafficSliver, which successfully counter
today’s WFP classifiers with reasonable bandwidth and latency
overheads and, thus, make them attractive candidates for adoption
in Tor. Through user-controlled splitting of traffic over multiple
Tor entry nodes, TrafficSliver limits the data a single entry node
can observe and distorts repeatable traffic patterns exploited by
WFP attacks.We first propose a network-layer defense, in which we
apply the concept of multipathing entirely within the Tor network.
We show that our network-layer defense reduces the accuracy from
more than 98% to less than 16% for all state-of-the-art WFP attacks
without adding any artificial delays or dummy traffic. We further
suggest an elegant client-side application-layer defense, which is
independent of the underlying anonymization network. By sending
single HTTP requests for different web objects over distinct Tor
entry nodes, our application-layer defense reduces the detection
rate of WFP classifiers by almost 50 percentage points. Although it
offers lower protection than our network-layer defense, it provides
a security boost at the cost of a very low implementation overhead and is fully compatible with today's Tor network.
Out-of-the-box Multipath TCP as a Tor Transport Protocol: Performance and Privacy Implications
(2020)
Support vector machine (SVM) is a classification model, which learns the decision surface that maximizes the margin in the feature space. Such a decision surface has a good classification ability for unknown new samples. In real-world applications, the data set usually contains many noises and outliers, which will affect the learning of the decision surface, thus the maximum margin cannot be obtained, and the generalization ability of SVM will be reduced. In this paper, we introduce an adjacency factor to each input point to characterize the local neighbor relationship between each point. Weighting each sample point by the adjacency factor can let different sample points make different contributions to the learning of the decision surface. Thus, we can filter out the influence of noises and outliers on the decision surface by this weighting method. We propose this new method namely local neighborhood reliability weighted support vector machine (LN-SVM).
Nowadays the mobile phone has become an indispensable tool in the lives of many people. While facilitating people's lives, it also provides criminals with a very important tool for spreading the terrorist video. Traditional manual detection of the terrorist video has the problem of low accuracy and inefficiency. To address the issue, this paper proposes a terrorist video detection system based on Light Gradient Boosting Machine (LightGBM) and Faster Region-based Convolutional Neural Network (Faster R-CNN) for mobile phone forensics system, which is used to quickly detect whether there is a terrorist video in the suspect's mobile phone. The system uses a multi-model method for detection, which includes preliminary detection and deep detection in two stages. Experimental research shows that it can effectively and accurately detect terrorist videos in mobile phones, thereby helping criminal investigation personnel to quickly grasp criminal evidence and provide some clues for the detection of the case.
Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client’s identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.
With the recent emergence of efficient zero-knowledge (ZK) proofs for general circuits, while efficient zero-knowledge proofs of algebraic statements have existed for decades, a natural challenge arose to combine algebraic and non-algebraic statements. Chase et al. (CRYPTO 2016) proposed an interactive ZK proof system for this cross-domain problem. As a use case they show that their system can be used to prove knowledge of a RSA/DSA signature on a message m with respect to a publicly known Pedersen commitment gmhr. One drawback of their system is that it requires interaction between the prover and the verifier.
This is due to the interactive nature of garbled circuits, which are used in their construction. Subsequently, Agrawal et al. (CRYPTO 2018) proposed an efficient non-interactive ZK (NIZK) proof system fo cross-domains based on SNARKs, which however require a trusted setup assumption. In this paper, we propose a NIZK proof system for cross domains that requires no trusted setup and is efficient both for the prover and the verifier.
Our system constitutes a combination of Schnorr based ZK proofs and ZK proofs for general circuits by Giacomelli et al. (USENIX 2016). The proof size and the running time of our system are comparable to the approach by Chase et al. Compared to Bulletproofs (SP 2018), a recent NIZK proofs system on committed inputs, our techniques achieve asymptotically better performance on prover and verifier, thus presenting a different trade-off between the proof size and the running time.
Tailoring Onion Routing to the Internet of Things: Security and Privacy in Untrusted Environments
(2019)
An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet. Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible. To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-toend security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.
Website fingerprinting (WFP) is a special type of traffic analysis, which aims to infer the websites visited by a user. Recent studies have shown that WFP targeting Tor users is notably more effective than previously expected. Concurrently, state-of-the-art defenses have been proven to be less effective. In response, we present a novel WFP defense that splits traffic over multiple entry nodes to limit the data a single malicious entry can use. Here, we explore several traffic-splitting strategies to distribute user traffic. We establish that our weighted random strategy dramatically reduces the accuracy from nearly 95% to less than 35% for four state-of-the-art WFP attacks without adding any artificial delays or dummy traffic.
Threshold verifiable multi‐secret sharing based on elliptic curves and Chinese remainder theorem
(2019)
The Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard routing protocol for the Internet of Things (IoT). This paper investigates the use of RPL in dynamic networks and presents an enhanced RPL for different applications with dynamic mobility and diverse network requirements. This implementation of RPL is designed with a new dynamic Objective-Function (D-OF) to improve the Packet Delivery Ratio (PDR), end-to-end delay and energy consumption while maintaining low packet overhead and loop-avoidance. We propose a controlled reverse-trickle timer based on received signal strength identification (RSSI) readings to maintain high responsiveness with minimum overhead and consult the objective function when a movement or an inconsistency is detected to help nodes make an informed decision. Simulations are done using Cooja with random waypoint mobility scenario for healthcare applications considering multi-hop routing. The results show that the proposed dynamic RPL (D-RPL) adapts to the nodes mobility and has a higher PDR, slightly lower end-to-end delay and reasonable energy consumption compared to related existing protocols.
We define the concept of and present provably secure constructions for Anonymous RAM (AnonRAM), a novel multi-user storage primitive that offers strong privacy and integrity guarantees. AnonRAM combines privacy features of anonymous communication and oblivious RAM (ORAM) schemes, allowing it to protect, simultaneously, the privacy of content, access patterns and user’s identity, from curious servers and from other (even adversarial) users. AnonRAM further protects integrity, i.e., it prevents malicious users from corrupting data of other users. We present two secure AnonRAM schemes, differing in design and time complexity. The first scheme has a simpler design; like efficient ORAM schemes, its time complexity is poly-logarithmic in the number of cells (per user); however, it is linear in the number of users. The second AnonRAM scheme reduces the overall complexity to poly-logarithmic in the total number of cells (of all users) at the cost of requiring two (non-colluding) servers.
The development of the prototype of the system SGAi (Generation System for InteractiveApplications) designed to obtain feedback from TV viewers about mass services, is reported; in a particularway, it is applied to evaluate the quality of telecommunication services. SGAi consists of three components: 1)Survey Composer, which lets users create and customize interactive applications for surveys and automaticallygenerate the NCL code (Nested Context Language) that will run on the STB (Set Top Box) of the viewer;2) A manageable web application that provides the survey management system, receives and storages opinionsas well as the display of results; and 3) the return channel, responsible for communications between the STBand the server of the manageable web application. The system is aimed, in particular Survey Composer, tofacilitate the generation of interactive applications that use the return channel, allowing the user to focus onthe content and aesthetics of the surveys rather than learning and using NCL. SGAi has been developed as partof research activities carried out in the digital TV area in the National Polytechnic School, considering theISDB-Tb standard, adopted in Ecuador in March 2010.