Global Navigation Satellite Systems (GNSS) are a key enabler for many new technologies, ranging from autonomous vehicles to shared mobile devices. In order to ensure high precision for those applications, GNSS augmentation systems are needed to provide correction data to reach an accuracy that is in the order of centimeters. Those systems can be provided as paid services where correction data are broadcast over a satellite link. In order to protect those systems and restrict their access only to paying users, we must adopt encryption mechanisms designed to avoid that pirates redistribute or resell the decryption key to unauthorized parties. At the same time, integrity protection mechanisms are needed to avoid that active attackers inject malicious GNSS augmentation data, which could disrupt or mislead the positioning. These objectives are made challenging by the peculiarities of satellite communication for the GNSS augmentation, in which bandwidth is scarce and receivers are resource-constrained. In this paper, we propose APBE (Anti-Piracy Broadcast Encryption) and SIA (Succinct Immediate Authentication), two methods to enhance the GNSS augmentation service security by providing protection against respectively pirate customers and active attackers. Both methods are specifically tailored to minimize bandwidth and processing time on the receiver. We demonstrate their feasibility via a proof-of-concept implementation on an ESP32 embedded system. We also measure APBE and SIA performance under various configuration, each giving a different security/performance tradeoff. APBE and SIA are candidate mechanisms to be included in the future versions of SPARTN, which is an open industry standard for GNSS augmentation.

Deepfakes are a type of synthetic media created using artificial intelligence, specifically deep learning algorithms. This technology can for example superimpose faces and voices onto videos, creating hyper-realistic but artificial representations. Deepfakes pose significant risks regarding misinformation and fake news, because they can spread false information by depicting public figures saying or doing things they never did, undermining public trust. In this paper, we propose a method that leverages BLS signatures (Boneh, Lynn, and Shacham 2004) to implement signatures that remain valid after image cropping, but are invalidated in all the other types of manipulation, including deepfake creation. Our approach does not require who crops the image to know the signature private key or to be trusted in general, and it is ${\mathcal{O}}(1)$ in terms of signature size, making it a practical solution for scenarios where images are disseminated through web servers and cropping is the primary transformation. Finally, we adapted the signature scheme for the JPEG standard, and we experimentally tested the size of a signed image.

The use of blockchains in the Internet of Things is extremely promising, as it gives connected things the possibility to send and receive payments or tamper-proof data. In the last years, FlyClient has emerged in the literature as a technique for allowing resource constrained devices to verify blockchain transactions. FlyClient is based on Merkle Mountain Ranges (MMRs) and probabilistic sampling, and it allows us to develop blockchain clients whose resource consumption is sublinear with the length of the chain. However, this comes at the cost of a change in the blockchain format, which leads to forks that are politically expensive, because they require 51% consensus. In this paper we explore the possibility of fork-free FlyClient verification methods that leverage smart contract programming. Smart contracts are able to add functionalities to a blockchain without needing forks. This raises several and novel technical issues that we address in the paper. We show that fork-free sublinear clients are feasible without trusting the nodes that invoke the smart contract methods, as long as the smart contract language provides a means to access the most recent block or its hash. As a proof of concept we propose SmartFly, a fork-free FlyClient verification system for the Ethereum Classic blockchain. We measure several performance metrics of SmartFly, proving that it is succinct in storage and bandwidth consumption and economically bearable (about 38 euros per day to maintain the whole system).

The Internet of Things (IoT) is an information service paradigm based on the integration of smart objects, mobile devices, and computers via the Internet. IoT technologies are key enablers for a multitude of applications in diverse fields, such as digital health, smart city, industrial automation, and supply chain. This raises new security and privacy challenges that can be addressed by advanced cryptographic methods. One of the most prominent is attribute-based encryption (ABE), which allows one to encrypt data while enforcing fine-grained access control on it. ABE is advantageous in many IoT applications since it allows data to be safely stored on untrusted storage, such as third-party cloud servers, hackable publish-subscribe brokers, physically accessible sensors, etc. This article surveys the ABE literature proposing schemes and solutions that are best suited for IoT applications. To do so, it first identifies three performance indicators that are key in IoT, namely, the data producer CPU efficiency, the data producer bandwidth efficiency, and the key authority bandwidth efficiency. Then, it analyzes only those schemes that are promising from the point of view of one or more indicators and, therefore, more applicable in typical IoT applications. As a further contribution, this article selects a subset of representative schemes and assesses their efficiency by thorough simulations. Such simulations show that no scheme excels in all three performance indicators at once, but some simultaneously perform well in two indicators.

Lately, many cloud-based applications proposed attribute-based encryption (ABE) as an all-in-one solution for achieving confidentiality and access control. Within this paradigm, data producers store the encrypted data on a semi-trusted cloud server, and users, holding decryption keys issued by a key authority, can decrypt data according to some access control policy. To be used in practical cases, any ABE scheme should implement a key revocation mechanism which assures that a compromised decryption key cannot be used anymore to decrypt data. Yuet al.(2010) introduced an ABE scheme with revocation capabilities that enjoys several unique advantages, such as reactivity and efficiency. In the scheme, the cloud server is entitled to update keys and ciphertexts in order to achieve revocation. Unfortunately, the cloud server retains the power to undo the revocation of a key (revocation undoing attack) so endangering confidentiality. In this article, we propose a revocable ABE scheme that still ensures the advantages of Yuet al.’s scheme, but it also resists to the revocation undoing attack. We formally prove the security of our scheme and show through simulations that the user experiences a slightly higher computational cost with respect to Yuet al.’s scheme.

Bitcoin-based smart city services are an ever increasing up-and-coming reality. For these services, simulating the Bitcoin blockchain is important to parametrize the system and tailor the costs and the economic incentives. In this regard, SimBlock simulator is the current state-of-the-art tool for blockchain simulations. Unfortunately, based on an up-to-date parametrization, SimBlock turns out not to simulate the mining of blocks. Furthermore, it does not simulate the incentive mechanism. These limitations strongly confine SimBlock's effective usage towards evaluating Bitcoin-based services relevant to many application contexts, including smart cities. To overcome these limitations, we propose an improved SimBlock's implementation. Upon it, we assess whether SimBlock can abstract the current Bitcoin blockchain. The experimental analysis shows that the proposed implementation can effectively simulate the current Bitcoin blockchain. Though, introducing relay network modelling in SimBlock should even improve the accuracy of the simulation.

Over- The-Air (OTA) update is an innovative paradigm that is rapidly spreading through the automotive industry. Software updates can be capillary distributed thanks to the many Vehicle-to-Everything (V2X) communication infrastructures that are part of a Smart City. Unfortunately, the majority of the existing OTA frameworks and schemes are not quantum resistant, meaning that when quantum computing will become reality, they will not be secure anymore. The U.S. National Institute of Standards and Technology (NIST) has announced a contest to determine the post-quantum standards for digital signatures schemes. In this paper, we evaluate the performance of the digital signature verification algorithms of two out of the three finalists for the NIST contest, namely FALCON and CRYSTALS-DILITHIUM. These algorithms are tested on automotive-oriented evaluation board, namely the Xilinx Zynq Ultrascale+ ZCU102. The results show that FALCON is a more promising algorithm compared to DILITHIUM both regarding signature verification execution time and signature size.

Distributed ledgers allow us to replicate databases of records across mutually untrusted parties. The best known example of distributed ledger is perhaps the Bitcoin blockchain, which maintains a consistent history of financial transactions organized as a hashed chain of blocks. Distributed ledgers can be public, i.e., accessible by everyone, or private, i.e., accessible only by a given consortium of parties. In this paper, we explore the technological possibilities of applying Identity-Based Encryption and Attribute-Based Encryption to distributed ledgers. We introduce the novel concept of Virtual Private Ledger. A Virtual Private Ledger is a private distributed ledger embedded in a public cryptocurrency ledger by means of cryptography. A Virtual Private Ledger provides for the same confidentiality and integrity of a private distributed ledger, but without its high operational costs. In particular, nodes that maintain the ledger do not have to be always online to trust the order and the integrity of the records. We analytically show that Virtual Private Ledgers can be implemented over many existing cryptocurrency ledgers like Ethereum, EOS.IO, IOTA, XRP. Different cryptocurrencies lead to different trade-offs between the Virtual Private Ledger max record size, cost, validation time, and max consortium members.

Urban population is expected to continuously grow in size. The smart city concepts allows to handle the new challenges and issues created by this growth by applying a wide range of technologies that can provide citizens with a better living environment. Smart agriculture will play an important part of smart cities, as a sustainable and high quality food supply chain is crucial to facilitate the grow of human agglomerates. In this context, European laws imposes very strict requirements in the food industry, in order to ensure that food provenance is always guaranteed. Such fine-grained traceability can be only achieved by applying state-of-the-art technologies. In this paper, we present BRUSCHETTA, a blockchain-based application for the traceability and the certification of the Extra Virgin Olive Oil (EVOO) supply chain. EVOO is an emblematic food product for Italy, but it is also one of the most falsified ones. BRUSCHETTA provides a blockchain-based system to enforce the certification of this product by tracing its entire supply chain: from the plantation to the shops. The goal is to enable the final customer to access a tamper-proof history of the product, including the farming, harvesting, production, packaging, conservation, and transportation processes. BRUSCHETTA leverages Internet of Things (IoT) technologies in order to interconnect sensors dedicated to EVOO quality control, and to let them operate on the blockchain. We also provide a support for the correct tailoring of the BRUSCHETTA blockchain system, and we propose a mechanism for its dynamic auto-tuning to optimize it in case of high loads.

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.

The Internet of Things (IoT) is a technological vision in which constrained or embedded devices connect together through the Internet. This enables common objects to be empowered with communication and cooperation capabilities. Industry can take an enormous advantage of IoT, leading to the so-called Industrial IoT. In these systems, integrity, confidentiality, and access control over data are key requirements. An emerging approach to reach confidentiality and access control is Attribute-Based Encryption (ABE), which is a technique able to enforce cryptographically an access control over data. In this paper, we propose fABElous, an ABE scheme suitable for Industrial IoT applications which aims at minimizing the overhead of encryption on communication. fABElous ensures data integrity, confidentiality, and access control, while reducing the communication overhead of 35% with respect to using ABE techniques naively.

In the near future, a technological revolution will involve our cities, where a variety of smart services based on the Internet of Things will be developed to facilitate the needs of the citizens. Sensing devices are already being deployed in urban environments, and they will generate huge amounts of data. Such data are typically outsourced to some cloud storage because this lowers capital and operating expenses and guarantees high availability. However, cloud storage may have incentives to release stored data to unauthorized entities. In this work we present ABE-Cities, an encryption scheme for urban sensing which solves the above problems while ensuring fine-grained access control on data by means of Attribute-Based Encryption (ABE). Basically, ABE-Cities encrypts data before storing it in the cloud and provides users with keys able to decrypt only those portions of data the user is authorized to access. In ABE-Cities, the sensing devices perform only lightweight symmetric cryptography operations, thus they can also be resource-constrained. ABE-Cities provides planned expiration of keys, as well as their unplanned revocation. We propose methods to make the key revocation efficient, and we show by simulations the overall efficiency of ABE-Cities.

The capability to verify positions reported by devices is called secure location verification. The majority of the proposed solutions entail the use of many fixed anchors often along with special hardware, e.g., ultra-wideband and ultrasonic transceivers. However, the deployment and maintenance costs of such solutions make them scarcely attractive. A cheaper alternative is to use mobile entities as trusted infrastructure. In particular, Unmanned Aerial Vehicles (UAVs) represent a promising approach. Indeed, recent studies used them to face the secure location verification problem. In this paper, we introduce a low-cost approach based on a swarm of UAVs and a common radio frequency protocol, e.g., WiFi. By experimental simulations, we show that by using only three UAVs our system detects more than 99% of the attacks against an adversary that falsifies its position of at least 20 m. We also consider an adversary capable of tracking UAVs positions. The success probability of such an advanced adversary is smaller than 1% starting from a falsification distance larger than 35m.

The increasing diffusion of smart devices opens to a new era for collecting large quantities of data from urban areas. Sensing information can be collected by using existing network infrastructures, but also by adopting small, cheap and configurable aerial vehicles, namely drones. Our work focusses on studying how to optimize their adoption for smart city applications designed to gather sensing data from user's devices roaming on the ground. To this purpose, we used HUMsim, a tool which generates realistic human traces, to mimic pedestrian mobility. From this dataset, we extract some sociality features that we exploit to plan a social-aware drone trajectory with the goal of maximizing the opportunities of interaction between drone and devices. Our experiments compare social-aware and social-oblivious trajectories showing that knowing the way people move and interact boosts the amount of retrievable data.

Many dependable systems rely on the integrity of the position of their components. In such systems, two key problems are secure localization and secure location verification of the components. Researchers proposed several solutions, which generally require expensive infrastructures of several fixed stations (anchors) with trusted positions. In this paper, we explore the approach of replacing all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone acts as an anchor and securely determines the positions. This approach completely eliminates the need for many expensive anchors. The main challenge becomes how to find a convenient path for the drone to do this for all the devices. The problem presents novel aspects, which make existing path planning algorithms unsuitable. We propose LocalizerBee, VerifierBee, and PreciseVerifierBee: three path planning algorithms that allow a drone to respectively measure, verify, and verify with a guaranteed precision a set of positions in a secure manner. They are able to securely localize all the positions in a generic deployment area, even in the presence of drone control errors. Moreover, they produce short path lengths and they run in a reasonable processing time.

Distance bounding protocols make it possible to determine a trusted upper bound on the distance between two devices. Their key property is to resist reduction attacks, i.e., attacks aimed at reducing the distance measured by the protocol. Recently, researchers have also focused on enlargement attacks, aimed at enlarging the measured distance. Providing security against such attacks is important for secure positioning techniques. The contribution of this paper is to provide a probabilistic model for the success of an enlargement attack against a distance bounding protocol realized with the IEEE 802.15.4a ultra-wideband standard. The model captures several variables, such as the propagation environment, the signal-to-noise ratio, and the time-of-arrival estimation algorithm. We focus on non-coherent receivers, which can be used in low-cost low-power applications. We validate our model by comparison with physical-layer simulations and goodness-of-fit tests. The results show that our probabilistic model is sufficiently realistic to replace physical-layer simulations. Our model can be used to evaluate the security of the ranging/positioning solutions that can be subject to enlargement attacks. We expect that it will significantly facilitate future research on secure ranging and secure positioning.

Wireless sensor networks enable a wealth of new applications in areas such as military, medical, environmental, transportation, smart city, and so on. In many of these scenarios, we need to measure in a secure way the positions of the sensors. Existing range-based techniques for secure positioning require a burdensome infrastructure, with many fixed anchors. Reducing the infrastructure would reduce deployment cost and foster the adoption of secure positioning solutions in wireless sensor networks. In this article, we propose SPEM, a secure positioning system based on multilateration and ultra-wideband (UWB) distance bounding protocols. The key idea behind SPEM is to leverage the low probability that an adversary has of controlling enlargement attacks against UWB. We estimate such a probability by a thorough study and signal-level simulations of the UWB physical layer. We test SPEM both in a simulated environment and in a real indoor environment using real UWB transceivers. We show that SPEM needs far less infrastructure than state-of-the-art solutions ( − 22% to − 93%, depending on the anchor deployment method), while achieving high levels of security against smart and determined adversaries.

Distance bounding protocols are secure protocols to determine an upper bound to the distance between two devices. These protocols have shown to be useful for many tasks, from proximity verification to secure positioning. Unfortunately, real distance bounding protocols hardly fulfill the claimed property. Attacks at the PHY layer may cause significant reductions on the estimated upper bound. These attacks can be mitigated, not eliminated, by changing the receiver architecture and the PHY layer. Every distance bounding protocol is thus non-ideal. In this paper, we study the impact of non-ideal distance bounding on the reliability of secure positioning techniques. We show that a reduction of 10 meters, which is possible against a real PHY layer, allows the adversary to falsify a position of 21 meters. We also propose two countermeasures to mitigate the problem, and then estimate their efficacy by simulations.

Many dependable systems rely implicitly on the integrity of the positions of their components. For example, let us consider a sensor network for pollution monitoring: it is sufficient that a hostile actor physically moves some sensors to completely disrupt the monitoring. In such scenarios, a key question is: how to securely verify the positions of devices? To answer this question, researchers proposed several solutions. However, these generally require several fixed stations (anchors) with trusted positions. In this paper, we explore the possibility to use the emerging drone technology in order to overcome the limitation of using several fixed anchors. In particular, our approach is to replace all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone “acts like” an anchor and securely verifies the positions of the devices. The main challenge here is to find a convenient path for the drone to do this. The problem presents novel aspects, thus existing path planning algorithms cannot be used. We present VerifierBee: a path planning algorithm that allows a drone to perform a secure location verification of a set of devices. VerifierBee finds a good approximation of the shortest path, and at the same time it respects a set of requirements about drone controllability, localization precision, and communication range.

The success of location-based services is growing together with the diffusion of GPS-equipped smart devices. As a consequence, privacy concerns are raising year by year. Location privacy is becoming a major interest in research and industry world, and many solutions have been proposed for it. One of the simplest and most flexible approaches is obfuscation, in which the precision of location data is artificially degraded before disclosing it. In this paper, we present an obfuscation approach capable of dealing with measurement imprecision, multiple levels of privacy, untrusted servers and adversarial knowledge of the map. We estimate its resistance against statistical-based deobfuscation attacks, and we improve it by means of three techniques, namely extreme vectors, enlarge-and-scale and hybrid vectors.