Federated Learning (FL), a distributed learning mechanism where data is decentralized across multiple devices and periodic gradient updates are shared, is an alternative to centralized training that aims to address privacy issues arising from raw data sharing. Despite the expected privacy benefits, prior research showcases the potential privacy leakage derived from overfitting, exploited by passive attacks. However, limited attention has been given to understanding and defending against active threats that increase model leakage by interfering with the training process, instead of relying on overfitting. This work addresses this gap by introducing Active Attribute Inference (AAI⋆), a novel active attack that encodes sensitive attribute information by making any targeted training sample leave a distinguishable footprint on the gradient of maliciously modified neurons [8]. Results, using two real-world datasets, show that it is possible to successfully encode sensitive information incurring a small error in terms of neuron activation. More importantly, on a practical scenario, AAI⋆can improve upon a state-of-the-art approach by achieving over 90% of restricted ROC AUC, therefore increasing model leakage. To defend against such active attacks, this work introduces several attack detection strategies tailored for different levels of the defender’s knowledge. Including the novel White-box Attack Detection Mechanism (WADM⋆) that detects abnormal changes in weights distribution, and two black-box strategies based on the monitorization of model performance. Results show that the detection rate can be 100% on both datasets. Remarkably, WADM⋆reduces any attack to random guessing while preserving model utility, offering significant improvements over existing defenses, particularly when clients are non-IID. By proposing active attacks against well-generalized models and effective countermeasures, this research contributes to a better understanding of privacy in FL systems.

The increasing number of connected and autonomous vehicles generates an even greater demand for efficient content delivery in vehicular networks. Estimating the popularity of content is an important task to proactively cache and distribute content throughout the networks to add value to users' experiences and reduce network congestion. This paper presents a novel approach for predicting popular content on vehicular networks based on a Federated Learning-Adversarial Autoencoder model and anonymised data. Unlike prior works that relied on users' raw features, our model protects user privacy through data anonymisation. This allows us to learn from the hidden patterns of content popularity and deliver popular content without compromising user privacy. Experiments showed that our approach exceeded traditional collaborative filtering and deep learning methods in terms of accuracy and robustness, even with sparse data.

We introduce a novel blockchain-based interledger authorisation scheme, a unique solution designed to support delegated authorisation management in cross-domain cloud environments. This innovative approach enables client applications, acting on behalf of a user, to request access to specific resources owned by a different organization. It benefits cross-domain multi-cloud applications, such as large-scale collaboration projects involving multiple cloud service providers. Multi-cloud presents authorisation challenges because of complex access control operations in multiple distinct domains. OAuth2 is the state-of-the-art choice for delegated authorisation. However, it falls short in handling such complex interactions with multiple authorisation servers and resource servers, and this is our motivation. Compared with OAuth2, our approach overcomes the limitations of a centralized model and achieves coordinated distinct delegations among multiple entities without a third-party trusted component. authorisation servers are blockchain participants and designated access token issuers, ensuring minimum client data collection and promoting client privacy. Multiple smart contracts are deployed in our multi-ledger blockchain environment to guarantee the provenance of the authorisation operations and facilitate scalable cross-domain authorisation. Our “hybrid interledger model” combines security techniques such as blockchain-based device authentication, hashed time-lock contracts (HTLCs), and service discovery. The blockchain-based device authentication ensures secure and trusted interactions, HTLCs enable time-bound transactions, and service discovery simplifies finding and connecting to the required cloud services. Our experimental evaluation provides insights into the scalability of our approach, which achieves decentralized authorisation in a multi-cloud environment with acceptable latencies compared with related works and shows stable throughput against concurrent authorisation requests in a multi-ledger system.

With the massive data collection from different devices, spanning from mobile devices to all sorts of IoT devices, protecting the privacy of users is a fundamental concern. In order to prevent unwanted disclosures, several Privacy-Preserving Mechanisms (PPMs) have been proposed. Nevertheless, due to the lack of a standardized and universal privacy definition, configuring and evaluating PPMs is quite challenging, requiring knowledge that the average user does not have. In this paper, we propose a privacy toolkit - Privkit - to systematize this process and facilitate automated configuration of PPMs. Privkit enables the assessment of privacy-preserving mechanisms with different configurations, while allowing the quantification of the achieved privacy and utility level of various types of data. Privkit is open source and can be extended with new data types, corresponding PPMs, as well as privacy and utility assessment metrics and privacy attacks over such data. This toolkit is available through a Python Package with several state-of-the-art PPMs already implemented, and also accessible through a Web application. Privkit constitutes a unified toolkit that makes the dissemination of new privacy-preserving methods easier and also facilitates reproducibility of research results, through a repository of Jupyter Notebooks that enable reproduction of research results.

Location Privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. This adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. This approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

With the recent advancements in wireless networks, Joint Communication and Sensing (JCAS) has become a growing field that is expected to be included in next-generation standards. However, not only is the current performance of the sensing ability still lacking to be used in real-world scenarios, proper security of such privacy-invasive technology has not been fully explored. To this end, we propose the creation of a more robust framework, capable of cross-domain detection and long-term analysis for improved detection, which will also serve as the basis for a security and privacy analysis of the threat landscape and solutions in this field.

To decrease the IoT attack surface and provide protection against security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity and authentication management. This work proposes a blockchain-based identity management approach with consensus authentication as a scalable solution for IoT device authentication management. The proposed approach relies on having a blockchain secure tamper proof ledger and a novel lightweight consensus-based identity authentication. The results show that the proposed decentralised authentication system is scalable as we increase number of nodes.

Permission managers in mobile devices allow users to control permissions requests, by granting of denying application's access to data and sensors. However, existing managers are ineffective at both protecting and warning users of the privacy risks of their permissions' decisions. Recent research proposes privacy protection mechanisms through user profiles to automate privacy decisions, taking personal privacy preferences into consideration. While promising, these proposals usually resort to a centralized server towards training the automation model, thus requiring users to trust this central entity. In this paper we propose a methodology to build privacy profiles and train neural networks for prediction of privacy decisions, while guaranteeing user privacy, even against a centralized server. Specifically, we resort to privacy-preserving clustering techniques towards building the privacy profiles, that is, the server computes the centroids (profiles) without access to the underlying data. Then, using federated learning, the model to predict permission decisions is learnt in a distributed fashion while all data remains locally in the users' devices. Experiments following our methodology show the feasibility of building a personalized and automated permission manager guaranteeing user privacy, while also reaching a performance comparable to the centralized state of the art, with an F1-score of 0.9.

Runtime permission managers for mobile devices allow requests to be performed at the time in which permissions are required, thus enabling the user to grant/deny requests in context according to their expectations. However, in order to avoid cognitive overload, second and subsequent requests are usually automatically granted without user intervention/awareness. This paper explores whether these automated decisions fit user expectations. We performed a field study with 93 participants to collect their privacy decisions, the surrounding context and whether each request was expected. The collected 65261 permission decisions revealed a strong misalignment between apps’ practices and expectation as almost half of requests are unexpected by users. This ratio strongly varies with the requested permission, the category and visibility of the requesting application and the user itself; that is, expectation is subjective to each individual. Moreover, privacy decisions are most strongly correlated with user expectation, but such correlation is also highly personal. Finally, Android’s default permission manager would have violated the privacy of our participants 15% of the time.

Current methods for optimization of low-density parity-check (LDPC) codes analyze the degree distribution pair asymptotically as block length approaches infinity. This effectively ignores the discrete nature of the space of valid degree distribution pairs for LDPC codes of finite block length. While large codes are likely to conform reasonably well to the infinite block length analysis, shorter codes have no such guarantee. We present and analyze an algorithm for completely enumerating the space of all valid degree distribution pairs for a given block length, code rate, maximum variable node degree, and maximum check node degree. We then demonstrate this algorithm on an example LDPC code of finite block length. Finally, we discuss how the result of this algorithm can be utilized by discrete optimization routines to form novel methods for the optimization of small block length LDPC codes.

Polar codes have been shown to provide an effective mechanism for achieving physical-layer security over various wiretap channels. A majority of these schemes require channel state information (CSI) at the encoder for both intended receivers and eavesdroppers. In this paper, we consider a polar coding scheme for secrecy over a Gaussian wiretap channel when no CSI is available. We show that the availability of a shared keystream between friendly parties allows polar codes to be used for both secure and reliable communications, even when the eavesdropper knows a large fraction of the keystream. The scheme relies on a predetermined strategy for partitioning the bits to be encoded into a set of frozen bits and a set of information bits. The frozen bits are filled with bits from the keystream, and we evaluate the security gap when the cyclic redundancy check-aided successive cancellation list decoder is used at both receivers in the wiretap channel model.

In this article, we investigate the secure spectral efficiency of an ultrareliable low-latency communication system, where communications occur with short packets due to delay constraints, so that a finite blocklength formulation is considered. In addition, we assume that no feedback channel is available to implement automatic repeat request schemes, so that packet replication (PR) and interface diversity (ID) strategies are used to improve performance, which are then compared in terms of physical-layer security while considering a Nakagami-$m$fading channel. Furthermore, we assume no knowledge of the instantaneous channel state information at Alice, neither with respect to Bob nor Eves, while the position of multiple colluding eavesdroppers are specified according to a Poisson point process. Numerical results show that the joint optimization of the blocklength, the transmit power, and the amount of information bits per codeword are crucial to maximize the secure spectral efficiency. In addition, we also show that ID outperforms the PR strategy in most scenarios when the number of replications/interfaces increases.

Device identity management and authentication are one of the critical and primary security challenges in IoT. In order to decrease the IoT attack surface and provide protection from security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity management systems and resilient device authentication mechanisms. Existing mechanisms for device identity management and device authentication were not designed for huge number of devices and therefore are not suitable for IoT environments. This work presents results of a blockchain-based identity management approach with consensus authentication, as a scalable solution for IoT device authentication management. Our identity management approach relies on having a blockchain secure tamper proof registry and lightweight consensus-based identity authentication.

Abstract Location privacy has became an emerging topic due to the pervasiveness of Location-Based Services (LBSs). When sharing location, a certain degree of privacy can be achieved through the use of Location Privacy-Preserving Mechanisms (LPPMs), in where an obfuscated version of the exact user location is reported instead. However, even obfuscated location reports disclose information which poses a risk to privacy. Based on the formal notion of differential privacy, Geo-indistinguishability has been proposed to design LPPMs that limit the amount of information that is disclosed to a potential adversary observing the reports. While promising, this notion considers reports to be independent from each other, thus discarding the potential threat that arises from exploring the correlation between reports. This assumption might hold for the sporadic release of data, however, there is still no formal nor quantitative boundary between sporadic and continuous reports and thus we argue that the consideration of independence is valid depending on the frequency of reports made by the user. This work intends to fill this research gap through a quantitative evaluation of the impact on the privacy level of Geo-indistinguishability under different frequency of reports. Towards this end, state-of-the-art localization attacks and a tracking attack are implemented against a Geo-indistinguishable LPPM under several values of privacy budget and the privacy level is measured along different frequencies of updates using real mobility data.

We propose adding an irregular quadrature amplitude modulation (QAM) constellation to a wireless transmission scheme in order to obtain greater control over the signal-to-noise ratio (SNR) required to successfully decode the signal. By altering the separation between adjacent symbols, the minimum required SNR is raised without degradation in the performance of the scheme. This allows the system to adapt to preferable channel conditions for the authorized user, making it harder for eavesdroppers to intercept and decode the transmission, thus making the communication safer. In addition, we show that by overlaying a coset code onto the QAM constellation, a new, stronger security gap metric can be further improved. Results show the effectiveness of this strategy with an interleaved coding for secrecy with a hidden key (ICSHK) scheme.

We propose a coding methodology for physical layer security with adaptive characteristics, whereby adaptive we mean that the system must be tunable to different operational points/signal-to-noise ratio levels of both the legitimate receiver and the eavesdropper. Based on interleaving and scrambling as techniques that shuffle the original message before transmission, we consider puncturing over an interleaving/scrambling key and/or over the message as a mechanism to provide the required adaptability to channel conditions. The proposed techniques have shown suitable adaptability to different channel quality levels of the legitimate receiver and eavesdropper, while still guaranteeing the desired reliability for the legitimate receiver and secrecy against the eavesdropper.

This paper presents a practical physical-layer security scheme based on coding methodologies combined with self-jamming to combat advantaged eavesdroppers, i.e., eavesdroppers that may possess an equal or even better channel than the legitimate receiver. We introduce a strengthened security gap notion, where reliability is assured by typical bit-error rate (BER) measurements, but secrecy is guaranteed by considering the entire distribution of messages upon reception, instead of average measures. Relying on this new security gap notion, we then propose a scheme that combines concatenated coding with self-jamming by the legitimate receiver for effective security and reliability even when eavesdroppers possess a channel with equal or better conditions than the legitimate receiver.

We propose an adaptive secrecy scheme using polar codes with random frozen bits for a general wiretap channel, in which to protect the data from a potential eavesdropper, part or all of the frozen bits are randomly generated per message. To assess the secrecy level of the proposed scheme, three types of decoding strategies are evaluated: a matching decoder which knows the positions of all inserted bits inside the blocklength and tries to estimate them using the same decoding techniques, a blind decoder which treats all the frozen bits as the same value, and a random decoder which considers those dynamic bits as random at the receiver. Results are presented in terms of the system security gap, assuming an adaptive decoding strategy. It is shown that the system achieves combined secrecy and reliability. The proposed scheme does not assume knowledge of the eavesdropper's channel when defining the indices of information and frozen bits.

In this paper, we fill a void between information theoretic security and practical coding over the Gaussian wiretap channel using a three-stage encoder/decoder technique. Security is measured using Kullback-Leibler divergence and resolvability techniques along with a limited number of practical assumptions regarding the eavesdropper's decoder. The results specify a general coding recipe for obtaining both secure and reliable communications over the Gaussian wiretap channel, and one specific set of concatenated codes is presented as a test case for the sake of providing simulation-based evaluation of security and reliability over the network. It is shown that there exists a threshold in signal-to-noise ratio (SNR) over a Gaussian channel, such that receivers experiencing SNR below the threshold have no practical hope of receiving information about the message when the three-stage coding technique is applied. Results further indicate that the two innermost encoding stages successfully approximate a binary symmetric channel, allowing the outermost encoding stage (e.g., a wiretap code) to focus solely on secrecy coding over this approximated channel.

This paper proposes a method to provide secrecy for digital communications with arbitrarily large quadrature amplitude modulation (QAM) constellations for transmission over a Gaussian fading wiretap channel. This is accomplished by breaking the constellation down into nested quadrature phase-shift keying (QPSK) symbols and randomizing the assignment between message bits and modulated symbols using channel state information (CSI). If enough random bits can be generated from CSI it becomes possible to uniquely map an arbitrary message to any symbol in the large QAM constellation. The proposed method can thereby provide perfect secrecy while maintaining high reliability by exclusively assigning minimum-distance-mapped constellations through the randomization for use by the legitimate decoder.

This paper presents a testbed implementation and evaluation of coding for secrecy schemes in a real environment through software defined radio platforms. These coding schemes rely on interleaving and scrambling with randomly generated keys to shuffle information before transmission. These keys are then encoded jointly with data and then hidden (erased) before transmission, thus only being retrievable through parity information resulting from encoded data. An advantage of the legitimate receiver (e.g. a better signal-to-noise ratio) on the reception of those keys provides the means to achieve secrecy against an adversary eavesdropper. Through this testbed implementation, we show the practical feasibility of coding for secrecy schemes in real-world environments, unveiling the usefulness of interleaving and scrambling with a hidden key to reduce the required advantage over an eavesdropper. We further describe and present solutions to a set of issues that appear when doing practical implementations of security schemes in software defined radio platforms.

Sharing location data is becoming more popular as mobile devices become ubiquitous. Location-based service providers use this type of data to provide geographically contextualized services to their users. However, sharing exact locations with possibly untrustworthy entities poses a thread to privacy. Geo-indistinguishability has been recently proposed as a formal notion based on the concept of differential privacy to design location privacy-preserving mechanisms in the context of sporadic release of location data. While adaptations for the case of continuous location updates have been proposed, the study on how the frequency of updates impacts the privacy and utility level is yet to be made. In this paper we address this issue, by analyzing the effect of frequency updates on the privacy and utility levels of four mechanisms: the standard planar Laplacian mechanism suitable for sparse locations, and three variants of an adaptive mechanism that is an adaptation of the standard mechanism for continuous location updates. Results show that the frequency of updates largely impacts the correlation between points. As the frequency of updates decreases, the correlation also decreases. The adaptive mechanism is able to adjust the privacy and utility levels accordingly to the correlation between past positions and current position. However, the estimator function that is used to predict the current location has a great influence in the obtained results.

Current physical-layer security techniques typically rely on a degraded eavesdropper, thus warranting some sort of advantage that can be relied upon to achieve higher levels of security. We consider instead non-degraded eavesdroppers that possess equal or better capabilities than legitimate receivers. Under this challenging setup, most of the current physical-layer security techniques become hard to administer and new dimensions to establish advantageous periods of communication are needed. For that, we consider employing a spread spectrum uncoordinated frequency hopping (UFH) scheme aided by friendly jammers for improved secrecy. We characterize the secrecy level of this spread spectrum scheme, by devising a stochastic geometry mathematical model to assess the secure packet throughput (probability of secure communication) of devices operating under UFH that accommodates the impact of friendly jammers. We further implement and evaluate these techniques in a real-world test-bed of software-defined radios. Results show that although UFH with jamming leads to low secure packet throughput values, by exploiting frequency diversity, these methods may be used for establishing secret keys. We propose a method for secret-key establishment that builds on the advantage provided by UFH and jamming to establish secret keys, notably against non-degraded adversary eavesdroppers that may appear in advantageous situations.

This paper presents a new technique for providing the analysis and comparison of wiretap codes in the small blocklength regime over the binary erasure wiretap channel. A major result is the development of Monte Carlo strategies for quantifying a code's equivocation, which mirrors techniques used to analyze forward error correcting codes. For this paper, we limit our analysis to coset-based wiretap codes, and give preferred strategies for calculating and/or estimating the equivocation in order of preference. We also make several comparisons of different code families. Our results indicate that there are security advantages to using algebraic codes for applications that require small to medium blocklengths.

The exploration of the physical layer characteristics of the wireless channel is currently the object of intensive research in order to develop advanced secrecy schemes that can protect information against eavesdropping attacks. Following this line of work, in this manuscript we consider a massive MIMO system and jointly design the channel precoder and security scheme. By doing that we ensure that the precoding operation does not reduce the degree of secrecy provided by the security scheme. The fundamental working principle of the proposed technique is to apply selective random rotations in the transmitted signal at the antenna level in order to achieve a compromise between legitimate and eavesdropper channel capacities. These rotations use the phase of the reciprocal wireless channel as a common random source between the transmitter and the intended receiver. To assess the security performance, the proposed joint scheme is compared with a recently proposed approach for massive MIMO systems. The results show that, with the proposed joint design, the number of antenna elements does not influence the eavesdropper channel capacity, which is proved to be equal to zero, in contrast to previous approaches.

Cloud computing is the latest trend in business for providing software, platforms and services over the Internet. However, a widespread adoption of this paradigm has been hampered by the lack of security mechanisms. In view of this, the aim of this work is to propose a new approach for detecting anomalies in cloud network traffic. The anomaly detection mechanism works on the basis of a Support Vector Machine (SVM). The key requirement for improving the accuracy of the SVM model, in the context of cloud, is to reduce the total amount of data. In light of this, we put forward the Poisson Moving Average predictor which is the core of the feature extraction approach and is able to handle the vast amount of information generated over time. In addition, two case studies are employed to validate the effectiveness of the mechanism on the basis of real datasets. Compared with other approaches, our solution exhibits the best performance in terms of detection and false alarm rates.

Due to the differences in terms of antenna height, scatterer density, and relative speed, V2I links exhibit different propagation characteristics compared to V2V links. We develop a geometry-based path loss and shadow fading model for V2I links. We separately model the following types of V2I links: line-of-sight, non-line-of-sight due to vehicles, non-line-of-sight due to foliage, and non-line-of-sight due to buildings. We validate the proposed model using V2I field measurements. We implement the model in the GEMV2 simulator, and make the source code publicly available.

It is our great pleasure to welcome you to the 3rd IEEE Workshop on Smart Vehicles: Connectivity Technologies and ITS Applications (SmartVehicles'16), which is held in conjunction with the 13th IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM'16). The aim of this workshop is to bring together academics, industry professionals, and application developers to presents recent developments, current research challenges, and future directions in the area of connected and smart transportation.

We propose a systematic concatenated coding scheme based on the combination of interleaving with powerful channel codes and jamming for wireless secrecy under the practical assumption of codes in the finite blocklength regime. The basic idea lies in generating a short random key that is used to shuffle/interleave information at the source, Alice. This key is then sent to the legitimate receiver, Bob, during a brief period of advantageous communication over the eavesdropper Eve (e.g., due to more interference from a jammer). Finally, the key is decoded at Bob to properly deinterleave the original information. Bob receives a better quality version of the interleaving key, therefore having the needed advantage over Eve. Information reliability is provided by a strong inner code, while security against Eve results from the proper selection of the outer code and interference levels over the key. We propose a methodology for selection of the outer code with reliability and security constraints. For that, we introduce bit error complementary cumulative distribution function metrics, suitable for security and reliability analysis of error correcting codes.

Most of current physical-layer security techniques rely on a degraded eavesdropper, thus warranting some sort of advantage that can be relied upon to achieve higher levels of security. We consider instead non-degraded eavesdroppers, that possess equal or better capabilities than legitimate receivers. Under this challenging setup, most of current physical-layer security techniques become hard to administer and new dimensions to establish advantageous periods of communication are needed. For that, we characterize the secrecy level of two schemes for physical-layer security under non-degraded eavesdroppers: a spread spectrum uncoordinated frequency hopping scheme, and a jamming receiver with self-interference cancellation.

Uncoordinated Frequency Hopping (UFH) has been proposed as a mechanism to address denial-of-service attacks, and consists of legitimate devices hopping uniformly at random between frequencies to cope with an attacker that aims to disrupt communication. We consider the use of UFH against an eavesdropper adversary that aims to overhear as much information as possible. We characterize the secrecy level of wireless networks under UFH, showing the harmful security effect of broadband eavesdropper adversaries capable of overhearing in multiple frequencies. To counter such eavesdroppers, we consider the use of broadband friendly jammers that are available to cause interference on eavesdroppers. Our results show that adding a limited number of broadband friendly jammers effectively improves the security level of such systems.

We present a collision-free jammer selection policy for enhanced wireless secrecy. Jammers, selected from the neighbors of a source, are friendly in the sense that they are willing to help the source to transmit securely by causing interference/collisions to possible eavesdroppers. The proposed jammer selection policy results in the selection of the largest number of jammers that do not cause collisions among themselves. This enables jammers to assist the source to transmit securely by causing interference to eavesdroppers, while sending their own traffic into the network.

We present a jamming protocol for secrecy-enhanced wireless networks in which otherwise silent devices are selected as jammers to cause interference to potential eavesdroppers. This cooperative protocol includes several jammer selection policies that lead to different levels of secrecy-energy tradeoffs. Our results show that there is some advantage over selecting well-connected jammers and there is a need for a minimum number of jammers for the energy cost of jamming to payoff.

ABSTRACT The vision towards the Network of the Future cannot be separated from the fact that today's networks, and networking services are subject to sophisticated and very effective attacks. When these attacks first appeared, spoofing and distributed denial‐of‐service attacks were treated as apocalypse for networking. Now, they are considered moderate damage, whereas more sophisticated and inconspicuous attacks, such as botnets activities, might have greater and far reaching impact. As the Internet is expanding to mobile phones and ‘smart dust’ and as its social coverage is liberalized towards the realization of ubiquitous computing (with communication), the concerns on security and privacy have become deeper and the problems more challenging than ever. Re‐designing the Internet as the Network of the Future is self‐motivating for researchers, and security and privacy cannot be provided again as separate, external, add‐on, solutions. In this paper, we discuss the security and privacy challenges of the Network of the Future and try to delimit the solutions space on the basis of emerging techniques. We also review methods that help the quantification of security and privacy in an effort to provide a more systematic and quantitative treatment of the area in the future. Copyright © 2011 John Wiley & Sons, Ltd.

Inspired by recent results on information-theoretic security, we consider the transmission of confidential messages over wireless networks, in which the legitimate communication partners are aided by friendly jammers. We characterize the security level of a confined region in a quasi-static fading environment by computing the probability of secrecy outage in connection with two new measures of physical-layer security: the jamming coverage and the jamming efficiency. Our analysis for various jamming strategies based on different levels of channel state information provides insight into the design of optimal jamming configurations and shows that a single jammer is not sufficient to maximize both figures of merit simultaneously. Moreover, a single jammer requires full channel state information to provide security gains in the vicinity of the legitimate receiver.

Signal interference and packet collisions are typically viewed as negative factors that hinder wireless communication networks. When security is the primary concern, signal interference may actually be very helpful. Starting with a stochastic network model, we are able to show that packet collisions caused by jamming nodes can indeed be used effectively to attain new levels of secrecy in multiterminal wireless environments. To this effect, we propose a practical jamming protocol that uses the well-known request-to-send/clear-to-send (RTS/CTS) handshake of the IEEE 802.11 standard as a signaling scheme. Various jammer selection strategies are investigated depending on the position of source, destination, and jamming nodes. The goal is to cause as much interference as possible to eavesdroppers that are located in unknown positions, while limiting the interference observed by the legitimate receiver. To evaluate the performance of each strategy, we introduce and compute a measure for the secure throughput. Our results show that jamming can increase the levels of secrecy significantly albeit at a substantial cost in terms of energy efficiency.

We analyze the role of jamming as a means to increase the security of wireless systems. Specifically, we characterize the impact of cooperative/friendly jamming on the secrecy outage probability of a quasi-static wiretap fading channel. We introduce jamming coverage and jamming efficiency as security metrics, and evaluate the performance of three different jamming strategies that rely on various levels of channel state information. The analysis provides insight for the design of optimal jamming configurations and indicates that one jammer is not enough to maximize both metrics simultaneously.

Under the emerging network coding paradigm, intermediate nodes in the network are allowed not only to store and forward packets but also to process and mix different data flows. We propose a low-complexity cryptographic scheme that exploits the inherent security provided by random linear network coding and offers the advantage of reduced overhead in comparison to traditional end-to-end encryption of the entire data. Confidentiality is achieved by protecting (or "locking") the source coefficients required to decode the encoded data, without preventing intermediate nodes from running their standard network coding operations. Our scheme can be easily combined with existing techniques that counter active attacks.