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The unprecedented proliferation of smart devices together with novel communication, computing, and control technologies have paved the way for the Advanced Internet of Things~(A-IoT). This development involves new categories of capable devices, such as high-end wearables, smart vehicles, and consumer drones aiming to enable efficient and collaborative utilization within the Smart City paradigm. While massive deployments of these objects may enrich people's lives, unauthorized access to the said equipment is potentially dangerous. Hence, highly-secure human authentication mechanisms have to be designed. At the same time, human beings desire comfortable interaction with their owned devices on a daily basis, thus demanding the authentication procedures to be seamless and user-friendly, mindful of the contemporary urban dynamics. In response to these unique challenges, this work advocates for the adoption of multi-factor authentication for A-IoT, such that multiple heterogeneous methods - both well-established and emerging - are combined intelligently to grant or deny access reliably. We thus discuss the pros and cons of various solutions as well as introduce tools to combine the authentication factors, with an emphasis on challenging Smart City environments. We finally outline the open questions to shape future research efforts in this emerging field., Comment: 7 pages, 4 figures, 2 tables. The work has been accepted for publication in IEEE Network, 2019. Copyright may be transferred without notice, after which this version may no longer be accessible

Contemporary urban environments are in prompt need of means for intelligent decision-making, where a crucial role belongs to smart video surveillance systems. While existing deployments of stationary monitoring cameras already deliver notable societal benefits, the proposed concept of massive video surveillance over connected vehicles that we contribute in this paper may further augment these important capabilities. We therefore introduce the envisioned system concept, discuss its implementation, outline the high-level architecture, and identify major data flows, while also offering insights into the corresponding design and deployment aspects. Our conducted case study confirms the potential of the described crowd sourced vehicular system to effectively complement and eventually surpass even the best of today's static video surveillance setups. We expect that our proposal will become of value and integrate seamlessly into the future Internet-of-Things landscape, thus enabling a plethora of advanced urban applications., Comment: 8 pages, 5 figures, accepted to IEEE Wireless Communications, 2019

Future autonomous vehicles will generate, collect, aggregate and consume significant volumes of data as key gateway devices in emerging Internet of Things scenarios. While vehicles are widely accepted as one of the most challenging mobility contexts in which to achieve effective data communications, less attention has been paid to the privacy of data emerging from these vehicles. The quality and usability of such privatized data will lie at the heart of future safe and efficient transportation solutions. In this paper, we present the XYZ Privacy mechanism. XYZ Privacy is to our knowledge the first such mechanism that enables data creators to submit multiple contradictory responses to a query, whilst preserving utility measured as the absolute error from the actual original data. The functionalities are achieved in both a scalable and secure fashion. For instance, individual location data can be obfuscated while preserving utility, thereby enabling the scheme to transparently integrate with existing systems (e.g. Waze). A new cryptographic primitive Function Secret Sharing is used to achieve non-attributable writes and we show an order of magnitude improvement from the default implementation., Comment: arXiv admin note: text overlap with arXiv:1708.01884

In this paper we present the Sampling Privacy mechanism for privately releasing personal data. Sampling Privacy is a sampling based privacy mechanism that satisfies differential privacy.

We consider the problem of routing in a mobile ad-hoc network (MANET) for which the planned mobilities of the nodes are partially known a priori and the nodes travel in groups. This situation arises commonly in military and emergency response scenarios. Optimal routes are computed using the most reliable path principle in which the negative logarithm of a node pair's adjacency probability is used as a link weight metric. This probability is estimated using the mobility plan as well as dynamic information captured by table exchanges, including a measure of the social tie strength between nodes. The latter information is useful when nodes deviate from their plans or when the plans are inaccurate. We compare the proposed routing algorithm with the commonly-used optimized link state routing (OLSR) protocol in ns-3 simulations. As the OLSR protocol does not exploit the mobility plans, it relies on link state determination which suffers with increasing mobility. Our simulations show considerably better throughput performance with the proposed approach as compared with OLSR at the expense of increased overhead. However, in the high-throughput regime, the proposed approach outperforms OLSR in terms of both throughput and overhead.

Autonomous and self-driving vehicles are appearing on the public highways. These vehicles commonly use wireless communication techniques for both vehicle-to-vehicle and vehicle-to-infrastructure communications. Manufacturers, regulators and the public are understandably concerned about large-scale systems failure or malicious attack via these wireless vehicular networks. This paper explores the use of sensing and signalling devices that are commonly integrated into modern vehicles for side-channel communication purposes. Visible light (using a CMOS camera) and acoustic (ultrasonic audio) side-channel encoding techniques are proposed, developed and evaluated in this context. The side-channels are examined both theoretically and experimentally and an upper bound on the line code modulation rate that is achievable with these side channel schemes in the vehicular networking context is established. A novel inter-vehicle session key establishment protocol, leveraging both side-channels and a blockchain public key infrastructure, is then presented. In light of the limited channel capacity and the interoperability/security requirements for vehicular communications, techniques for constraining the throughput requirement, providing device independence and validating the location of the intended recipient vehicle, are presented. These reduce the necessary device handshake throughput to 176 bits for creating symmetric encryption and message authentication keys and in verifying a vehicle's certificate with a recognised certification authority.

In this paper, we introduce the family of Anonymized Local Privacy mechanisms. These mechanisms have an output space of three values "Yes", "No", or "$\perp$" (not participating) and leverage the law of large numbers to generate linear noise in the number of data owners to protect privacy both before and after aggregation yet preserve accuracy. We describe the suitability in a distributed on-demand network and evaluate over a real dataset as we scale the population.

We study a network utility maximization (NUM) decomposition in which the set of flow rates is grouped by source-destination pairs. We develop theorems for both single-path and multipath cases, which relate an arbitrary NUM problem involving all flow rates to a simpler problem involving only the aggregate rates for each source-destination pair. The optimal aggregate flows are then apportioned among the constituent flows of each pair. This apportionment is simple for the case of $\alpha$-fair utility functions. We also show how the decomposition can be implemented with the alternating direction method of multipliers (ADMM) algorithm.

When dealing with privatized data, it is important to be able to protect against malformed user inputs. This becomes difficult in MPC systems as each server should not contain enough information to know what values any user has submitted. In this paper, we implement an MPC technique to verify blinded user inputs are unit vectors. In addition, we introduce a BGW circuit which can securely aggregate the blinded inputs while only releasing the result when it is above a public threshold. These distributed techniques take as input a unit vector. While this initially seems limiting compared to real number input, it is quite powerful for cases such as selecting from a list of options, indicating a location from a set of possibilities, or any system which uses one-hot encoding.

Smart cities rely on dynamic and real-time data to enable smart urban applications such as intelligent transport and epidemics detection. However, the streaming of big data from IoT devices, especially from mobile platforms like pedestrians and cars, raises significant privacy concerns. Future autonomous vehicles will generate, collect and consume significant volumes of data to be utilized in delivering safe and efficient transportation solutions. The sensed data will, inherently, contain personally identifiable and attributable information - both external (other vehicles, environmental) and internal (driver, passengers, devices). The autonomous vehicles are connected to the infrastructure cloud (e.g., Amazon), the edge cloud, and also the mobile cloud (vehicle to vehicle). Clearly these different entities must co-operate and interoperate in a timely fashion when routing and transferring the highly dynamic data. In order to maximise the availability and utility of the sensed data, stakeholders must have confidence that the data they transmit, receive, aggregate and reason on is appropriately secured and protected throughout. There are many different metaphors for providing end-to-end security for data exchanges, but they commonly require a management and control sidechannel. This work proposes a scalable smart city privacy-preserving architecture named Authorized Analytics that enables each node (e.g. vehicle) to divulge (contextually) local privatised data. Authorized Analytics is shown to scale gracefully to IoT scope deployments.

Today, mobile data owners lack consent and control over the release and utilization of their location data. Third party applications continuously process and access location data without data owners granular control and without knowledge of how location data is being used. The proliferation of IoT devices will lead to larger scale abuses of trust. In this paper we present the first design and implementation of a privacy module built into the GPSD daemon. The GPSD daemon is a low-level GPS interface that runs on GPS enabled devices. The integration of the privacy module ensures that data owners have granular control over the release of their GPS location. We describe the design of our privacy module and then evaluate the performance of private GPS release and demonstrate that strong privacy guarantees can be built into the GPSD daemon itself with minimal to no overhead., Comment: arXiv admin note: text overlap with arXiv:1604.04892

In today's digital world, personal data is being continuously collected and analyzed without data owners' consent and choice. As data owners constantly generate data on their personal devices, the tension of storing private data on their own devices yet allowing third party analysts to perform aggregate analytics yields an interesting dilemma. This paper introduces PAS-MC, the first practical privacy-preserving and anonymity stream analytics system. PAS-MC ensures that each data owner locally privatizes their sensitive data before responding to analysts' queries. PAS-MC also protects against traffic analysis attacks with minimal trust vulnerabilities.We evaluate the scheme over the California Transportation Dataset and show that we can privately and anonymously stream vehicular location updates yet preserve high accuracy.

In our study, we seek to learn the real-time crowd levels at popular points of interests based on users continually sharing their location data. We evaluate the benefits of users sharing their location data privately and non-privately, and show that suitable privacy-preserving mechanisms provide incentives for user participation in a private study as compared to a non-private study.

In content-based mobile ad hoc networks (CB-MANETs), random linear network coding (NC) can be used to reliably disseminate large files under intermittent connectivity. Conventional NC involves random unrestricted coding at intermediate nodes. This however is vulnerable to pollution attacks. To avoid attacks, a brute force approach is to restrict the mixing at the source. However, source restricted NC generally reduces the robustness of the code in the face of errors, losses and mobility induced intermittence. CB-MANETs introduce a new option. Caching is common in CB MANETs and a fully reassembled cached file can be viewed as a new source. Thus, NC packets can be mixed at all sources (including the originator and the intermediate caches) yet still providing protection from pollution. The hypothesis we wish to test in this paper is whether in CB-MANETs with sufficient caches of a file, the performance (in terms of robustness) of the restricted coding equals that of unrestricted coding. In this paper, we examine and compare unrestricted coding to full cache coding, source only coding, and no coding. As expected, we find that full cache coding remains competitive with unrestricted coding while maintaining full protection against pollution attacks.

This paper presents a secure communication application called DiscoverFriends. Its purpose is to securely communicate to a group of online friends while bypassing their respective social networking servers under a mobile ad hoc network environment. DiscoverFriends leverages Bloom filters and a hybrid encryption technique with a self-organized public-key management scheme to securely identify friends and provide authentication. Additionally, DiscoverFriends enables anonymous location check-ins by utilizing a new cryptographic primitive called Function Secret Sharing. Finally, to the best of our knowledge, DiscoverFriends implements and evaluates the first Android multi-hop WiFi direct protocol using IPv6., Comment: Eric and Joshua contributed equally to this work

Neighbor discovery plays a crucial role in the formation of wireless sensor networks and mobile networks where the power of sensors (or mobile devices) is constrained. Due to the difficulty of clock synchronization, many asynchronous protocols based on wake-up scheduling have been developed over the years in order to enable timely neighbor discovery between neighboring sensors while saving energy. However, existing protocols are not fine-grained enough to support all heterogeneous battery duty cycles, which can lead to a more rapid deterioration of long-term battery health for those without support. Existing research can be broadly divided into two categories according to their neighbor-discovery techniques---the quorum based protocols and the co-primality based protocols.In this paper, we propose two neighbor discovery protocols, called Hedis and Todis, that optimize the duty cycle granularity of quorum and co-primality based protocols respectively, by enabling the finest-grained control of heterogeneous duty cycles. We compare the two optimal protocols via analytical and simulation results, which show that although the optimal co-primality based protocol (Todis) is simpler in its design, the optimal quorum based protocol (Hedis) has a better performance since it has a lower relative error rate and smaller discovery delay, while still allowing the sensor nodes to wake up at a more infrequent rate., Comment: Accepted by IEEE INFOCOM 2015

Despite the evolution in deployed infrastructure and in the way that people access information, still there are those who are socially excluded and have no access to information due to their geographic location (e.g., riverside/countryside communities). This paper proposes an extension to a DTN architecture implementation to allow the dissemination of information in such communities, including educational short-video clips and audio books. The IBR-DTN architecture is complemented with a Bluetooth Convergence Layer, to facilitate the exchange of information over this short-range wireless technology, and with a Bundle Compression mechanism that aims at improving data exchange in short-lived opportunistic contacts happening among nodes. Experiments in a small-scale testbed and in a large-scale simulator environment show that nodes are indeed able to efficiently use contact opportunities to exchange an increased amount of data, allowing people in riverside communities to receive more content related to digital inclusion services.

Social network companies maintain complete visibility and ownership of the data they store. However users should be able to maintain full control over their content. For this purpose, we propose WARP, an architecture based upon Information-Centric Networking (ICN) designs, which expands the scope of the ICN architecture beyond media distribution, to provide data control in social networks. The benefit of our solution lies in the lightweight nature of the protocol and in its layered design. With WARP, data distribution and access policies are enforced on the user side. Data can still be replicated in an ICN fashion but we introduce control channels, named \textit{thread updates}, which ensures that the access to the data is always updated to the latest control policy. WARP decentralizes the social network but still offers APIs so that social network providers can build products and business models on top of WARP. Social applications run directly on the user's device and store their data on the user's \textit{butler} that takes care of encryption and distribution. Moreover, users can still rely on third parties to have high-availability without renouncing their privacy.