The rise of urbanization, overpopulation, and resource depletion in recent years has triggered interest in developing more efficient solutions that could offer sustainable development and improve the quality of life in cities. The increasingly wider and more advanced availability of computational power throughout the anthropic space—which saw the emergence of the so-called “ubiquitous computing” paradigm—has opened new possibilities for the design of smart cities. In particular, the emergence of Extended Reality technologies (XR), such as Virtual Reality and Augmented Reality, has provided a new interface to bridge the gap between the physical and digital realms, enabling immersive experiences and interactions within Smart City environments. This paper, based on three case studies at different scales of smart environments, explores the current and prospected relevance of XR to both design and experience spaces enriched and characterized by layers of digital information and sensorial interactions.

An in-depth exploration of the reaction kinetics and thermo-chemical behaviors of the raceway can offer practical insights for optimizing the operations of blast furnace (BF), thus achieving a more effective iron and steel production process. In this study, the dynamic characteristics and the flow, heat and mass transfer behaviors in the BF raceway were simulated by Discrete Element Method-Computational Fluid Dynamics (DEM-CFD) method at a particulate scale. The effects of coke size distribution and blast velocity on coke combustion characteristics, thermochemical behavior (particle volume fraction, raceway size, carbon loss, and coke temperature) and microscopic properties (coordination number (CN), contact normal force, pore structure and stress) were systematically investigated. The results show that as the blast velocity decreases or the size ratio λ (the largest coke particle size divided by the smallest coke particle size) increases, the raceway size becomes smaller, resulting in a smaller area of high oxygen (O₂) concentration and low carbon monoxide (CO) concentration in the raceway, and higher CO concentration in the packed bed. For the thermal-chemical behaviors, a lower blast velocity or a higher λ value decreases the number of particles experiencing mass loss, as well as increases individual particle mass loss, the average coke temperature and its variance. For microscopic properties, the CN distribution becomes wider as λ increases. The contact normal force in the coke bed with λ > 1 is significantly higher than that of λ = 1. As λ increases or blast velocity decreases, the pore distribution curve shifts to the left and the average pore volume decreases. The stress acting on the particles in the raceway increases with the blast velocity or λ. These new understandings of the complex reactive flow behaviors in the raceway will shed light on energy utilization and process optimization.

Smart Parking Services (SPSs) enable cruising drivers to find the nearest parking lot with available spots, reducing the traveling time, gas, and traffic congestion. However, drivers risk the exposure of sensitive location data during parking query to an untrusted Smart Parking Service Provider (SPSP). Our motivation arises from a repetitive query to an updated database, i.e., how a driver can be repetitively paired with a previously-matched-but-forgotten lot. Meanwhile, we aim to achieve repetitive query in an oblivious and unlinkable manner. In this work, we present Mnemosyne2 : decentralized and privacy-preserving smart parking with secure repetition and full verifiability. Specifically, we design repetitive, oblivious, and unlinkable Secure k Nearest Neighbor (SkNN) with basic verifiability (correctness and completeness) for encrypted-andupdated databases. We build a local Ethereum blockchain to perform driver-lot matching via smart contracts. To adapt to the lot count update, we resort to the immutable blockchain for advanced verifiability (truthfulness). Last, we utilize decentralized blacklistable anonymous credentials to guarantee identity privacy. Finally, we formally define and prove privacy and security. We conduct extensive experiments over a real-world dataset and compare Mnemosyne2 with existing work. The results show that a query only needs 8 seconds (175 ms) on average for service waiting (verification) among 500 drivers.

Threshold signature is a powerful cryptographic technique with a large number of real-life applications. As designed by Boneh and Komlo (CRYPTO’22), TAPS is a new threshold signature integrating privacy and accountability. It allows a combiner to combine t signature shares while protecting t and the signing group from the public. It also enables a tracer to trace a threshold signature to its original signing group. Despite being valuable, TAPS neglects the witnessing of tracing, i.e., leaves the tracing activity unrestrained. In this paper, we introduce Accountable and Private Threshold Signature with Hidden Witnesses (HiTAPS) that not only provides privacy and accountability, but also incorporates witnessed tracing. In specific, we first utilize Dynamic Threshold Public-Key Encryption (DTPKE) and ElGamal encryption to designate a set of t^′ witnesses for endorsing the tracing activity. We then compute a keyed-hash tag for the t^′ witnesses to initiate the tracing activity secretly. Moreover, we present an optimized protocol HiTAPS2 to reduce communication overhead of the combiner. We formalize the definitions, security, and privacy for HiTAPS. We formally prove its security and privacy. To evaluate the performance of HiTAPS and HiTAPS2, we build a prototype based on pypbc. Experimental results show that HiTAPS takes 217(370) ms to combine (track) a threshold signature of 5 signers (witnesses). The optimized HiTAPS2 only takes 137 ms to combine a threshold signature of 5 signers.

We analyzed observational and model data to study the sources of formaldehyde over oil and gas production regions and to investigate how these observations may be used to constrain oil and gas volatile organic compound (VOC) emissions. The analysis of aircraft and satellite data consistently found that formaldehyde over oil and gas production regions during spring and summer is mostly formed by the photooxidation of precursor VOCs. Formaldehyde columns over the Permian Basin, one of the largest oil- and gas-producing regions in the United States, are correlated with the production locations. Formaldehyde simulations by the atmospheric chemistry and transport model WRF-Chem, which included oil and gas NO_x and VOC emissions from the fuel-based oil and gas inventory, were in very good agreement with TROPOMI satellite measurements. Sensitivity studies illustrated that VOCs released from oil and gas activities are important precursors to formaldehyde, but other sources of VOCs contribute as well and that the formation of secondary formaldehyde is highly sensitive to NO_x. We also investigated the ability of the chemical mechanism used in WRF-Chem to represent formaldehyde formation from oil and gas hydrocarbons by comparing against the Master Chemical Mechanism. Further, our work provides estimates of primary formaldehyde emissions from oil and gas production activities, with per basin averages ranging from 0.07 to 2.2 kg h^-1 in 2018. A separate estimate for natural gas flaring found that flaring emissions could contribute 5 to 12% to the total primary formaldehyde emissions for the Permian Basin in 2018.

Mixing structures and characteristics are crucial to the mixing quality of spherical/cylindrical binary granular systems like the biomass-coal mixtures which can affect energy release and carbon emissions. In this work, the mixing of binary spheres/cylinders in a rotating drum was numerically reproduced by using discrete element method (DEM). Systematic parametric studies were conducted to identify the role of various parameters such as rotation speed (ω) of the drum, aspect ratio (AR), mass fraction (φ_c) and volume fraction (φ_v) of cylindrical particles, and the density difference (φ_ρ) between the binary particles; meanwhile, corresponding mechanisms were also explored by analyzing the kinetic energy. Results show that when the flow regime is rolling/cascading, the mixing quality can be effectively improved; however, when the flow regime is cataracting, the mixing quality becomes worse. With AR close to 1.0, the interlocking effect between particles becomes weaker and the porosity becomes smaller, which leads to the higher contact efficiency and thus improves the mixing quality. Binary mixtures with different φ_c are synergistically affected by energy input and interlocking structure. The volume of spherical particles is more conducive to improving the mixing quality than that of cylindrical particles when the volume of the granular system is at the same level. The φ_ρ can cause segregation behavior of particles so as to make the mixing quality worse.

The protection of multicast transmission with optimized secrecy in millimeter-wave (mmWave) communication is still an open problem. In this paper, we propose a low sidelobe secret multicast transmission scheme in mmWave communication based on physical layer security techniques. By utilizing oblique projection, we jointly adopt the directional modulation (DM) and the artificial noise (AN) to achieve secret multicast transmission at low computational costs, without jeopardizing the low sidelobe transmitting pattern. Specifically, considering the constraint of multibeam with the channel knowledge of all target users, a primary transmitting weight vector of the base station (BS) is designed to achieve a low sidelobe transmitting pattern. Then, in each symbol period, the transmitting weight vector of the BS is updated by performing a linear transformation on the primary weight vector with a transformation matrix, which is obtained from the oblique projection matrices of all the target users' channel vectors. In this way, without deteriorating the primary weight vector's low sidelobe transmitting pattern, the obtained transmitting weight vector synthesizes the expected symbols at the target users and adds randomness to the eavesdroppers at undesired directions. Finally, the simulations demonstrate that our proposed scheme achieves outstanding communication performance for the target users at low computational costs, while keeping the high symbol error rates at the undesired directions.

Many regions are in urgent need of facial masks for slowing down the spread of COVID-19. To fight the pandemic, people are contributing masks through donation systems. Most existing systems are built on a centralized architecture which is prone to the single point of failure and lack of transparency. Blockchain-based solutions neglect fundamental privacy concerns (<italic>donation privacy</italic>) and security attacks (<italic>collusion attack, stealing attack</italic>). Moreover, current auditing solutions are not designed to achieve donation privacy, thus not appropriate in our context. In this work, we design a decentralized, anonymous, and secure auditing framework <italic>Astraea</italic> based on private smart contracts for donation systems. Specifically, we integrate a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute donations, prove the integrity of donation number (intention) and donation sum while preserving donation privacy. With DiSC, we design a Donation Smart Contract to refund deposits and defend against the stealing attack the collusion attack from malicious collector and transponder. We formally define and prove the privacy and security of Astraea by using security reduction. We build a prototype of Astraea to conduct extensive performance analysis. Experimental results demonstrate that Astraea is practically efficient in terms of both computation and communication.

Location-Based Services (LBSs) depend on a Service Provider (SP) to store data owners’ geospatial data and to process data users’ queries. For example, a Yelp user queries the SP to retrieve the k nearest Starbucks by submitting her/his current location. It is well-acknowledged that location privacy is vital to users and several prominent Secure k Nearest Neighbor (SkNN) query processing schemes are proposed. We observe that no prior work addresses the requirement of repetitive query after index update and its privacy issue, i.e., how to match a data item from the cloud repetitively in an oblivious and unlinkable manner. Meanwhile, a malicious SP may skip some data items and recommend others due to unfair competition. In this work, we formally define the repetitive query and its privacy objectives and present an Repetitive, Oblivious, and Unlinkable SkNN scheme ROU. Specifically, we design a multi-level structure to organize locations to further improve search efficiency. Second, we integrate data item identity into the framework of existing SkNN query processing. Data owners encrypt their data item identity and location information into a secure index, and data users encrypt a customized identity range of a previously retrieved data item and location information into a token. Next, the SP uses the token to query the secure index to find the specific data item via privacy-preserving range querying. We formally prove the privacy of ROU in the random oracle model. We build a prototype based on a server to evaluate the performance with a real-world dataset. Experimental results show that ROU is efficient and practical in terms of computational cost, communication overhead, and result verification.

Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this article, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack, existing work requires heavy computations to execute user matching, and it is challenging for riders to verify matching results. Meanwhile, a malicious driver may cancel an assigned ride order due to its short distance. To address these issues, we present a RHS system named Nereus to support collusion resistance, efficiency, verifiability, and accountability. First, we integrate a smart contract into a Software Guard Extensions (SGX) enclave to establish a private smart contract for collusion resistance. We use a Bloom filter to achieve efficient matching. Second, we leverage privacy-preserving range query and Merkle proofs to make matching results verifiable. Meanwhile, we adopt short group signatures to provide anonymous authentication and deposit commitments to hold the runaway driver accountable. We formally state and prove the security and privacy of Nereus. We build a prototype based on Ethereum and SGX to conduct extensive performance analysis in regard to gas costs, computational costs, and communication overhead. Experimental results show that Nereus significantly improves over existing schemes in terms of computational costs.

Shortest distance queries over large-scale graphs bring great benefits to various applications, i.e., save planning time and travelling expenses. To protect the sensitive nodes and edges in the graph, a user outsources an encrypted graph to an untrusted server without losing the query ability. However, no prior work has considered the user requirement of the shortest path with k unsorted nodes. In particular, we are concerned with how to securely find the shortest path by passing k nodes that do not have a fixed traverse order. To solve the problems, we propose Gespun (stands for Graph encryption for shortest path queries with k unordered nodes). It includes an oracle encryption scheme that is provably secure against the semi-honest server. Specifically, we compute the shortest paths and distances for all nodes locally to obtain path-distance oracles. We transform the shortest paths to a sequence of secure codes by using a pseudo-random permutation to protect the structure privacy. We encrypt the shortest distance by using additively homomorphic encryption. Second, we pack the oracles in link-list nodes and store them in an array-based dictionary after another permutation. Next, we construct a search graph to compute the shortest path while guaranteeing that the path passes the required k nodes. We formally prove that Gespun is adaptively semantically-secure in the random oracle. We implement a prototype of Gespun and evaluate its performance. Experiments results demonstrate that Gespun is efficient, e.g., a query over 6301 nodes, 20777 edges, and 5 unsorted nodes only needs 483 ms to get queried results. We believe that our research problem span new research that soon promotes a new line of graph encryption schemes.

Ride-Hailing Service (RHS) has drawn plenty of attention as it provides transportation convenience for riders and financial incentives for drivers. Despite these benefits, riders risk the exposure of sensitive location data during ride requesting to an untrusted Ride-Hailing Service Provider (RHSP). Our motivation arises from repetitive matching, i.e., the same driver is repetitively assigned to the same rider. Meanwhile, we introduce a driver exclusion function to protect riders' location privacy. Existing work on privacy-preserving RHS overlooks this function. While Secure k Nearest Neighbor (SkNN) facilitates efficient matching, the state-of-the-art neglects a collusion attack. To solve this problem, we formally define repetitive matching and strong location privacy, and propose Mnemosyne: privacy-preserving ride matching with collusion-resistant driver exclusion. We extend the simple integration of equality checking and item exclusion to a dynamic integration. We concatenate each prefix of an acceptable identity range to each location code when generating a ride request, i.e., secure mix index. We process each prefix of the driver identity to generate a ride response, i.e., a mix token. We build an indistinguishable Bloom-filter as an index to query the token. When matching riders with drivers, the colluding parties cannot distinguish identity prefixes from location codes. We build a prototype of Mnemosyne based on servers, smartphones, and a real-world dataset. Experimental results demonstrate that Mnemosyne outperforms existing work regarding strong location privacy and computational costs.

Traffic-sensitive navigation systems in vehicular networks help drivers avoid traffic jams by providing several realtime navigation routes. However, drivers still encounter privacy concerns because their sensitive locations, i.e., their start point and endpoint, are submitted to an honest-but-curious navigation service provider (NSP). Previous privacy-preserving studies exhibit serious deficiencies under similar queries: if a driver makes several similar queries, i.e., periodically makes requests for the same start point and endpoint to the NSP, these requests will eventually reveal the areas of the two points as well as the route. In this article, we present a novel privacy-preserving navigation scheme PiSim, which supports similar queries in navigation services. Intuitively, we transform the typical navigation approach into a traffic congestion querying approach. Instead of sending two locations to the NSP and awaiting a navigation route, drivers query the traffic congestion along the navigation route. Specifically, PiSim is characterized by extending anonymous authentication, facilitating privacy-preserving multi-keyword fuzzy search, and constructing weighted proximity graphs. Our scheme protects location privacy and route privacy, and defends against multiple requesting, spurious reporting, and collusion attacks from malicious drivers. Finally, a detailed analysis confirms the privacy and security properties of PiSim. Extensive experiments are conducted to demonstrate the feasibility, performance, and privacy protection level.

Lawful evidence management in digital forensics is of paramount importance in police investigations because such evidence is used to convict suspects of crimes. Existing studies have adopted cloud computing to collect evidence and then leveraged blockchain to support the transparency, immutability, and auditability of the evidence. Unfortunately, such studies only rely on a weak security model and do not cover the entire life cycle of the evidence or address the key privacy issues, i.e., witness privacy in evidence collection and juror privacy in court trials. In this work, we propose LEChain, a blockchain-based lawful evidence management scheme to supervise the entire evidence flow and all of the court data (e.g., votes and trial results), extending from evidence collection and access during the police investigation to jury voting in the court trials. Specifically, we utilize short randomizable signatures to anonymously authenticate witnesses’ identities to protect the witness privacy. Then, we leverage fine-grained access control based on ciphertext-policy attribute-based encryption for evidence access. Next, we design a secure voting method to protect juror privacy. In addition, we build a consortium blockchain to record evidence transactions. Finally, we formally analyze the security and privacy of LEChain and evaluate its computational costs and communication overhead by implementing a prototype based on a local Ethereum test network.