The increasing presence of smart mobile devices in sensitive environments raises significant security and privacy concerns, particularly due to the unauthorized usage of built-in sensors such as cameras and microphones. However, space owners currently have limited means to enforce restrictions on mobile devices within their premises. To address this issue, we propose a novel location-based access control system utilizing bluetooth low energy (BLE) beacons to dynamically enforce security policies. The proposed system introduces the jumbo beacon concept, which enables fragmented transmission and reassembly of signed access control policies, overcoming BLE payload limitations. Unlike centralized enforcement models, our approach is fully decentralized, eliminating the need for a trusted central server and providing a flexible, scalable mechanism for enforcing fine-grained access policies. The system is implemented as a native security module within the Android operating system, ensuring tamper-resistant enforcement of policies while preventing unauthorized modifications. A proof-of-concept implementation demonstrates the system’s effectiveness, highlighting its real-time policy enforcement capabilities and resilience against adversarial threats. The results indicate that our approach offers a lightweight, scalable, and secure solution for enforcing location-based access control in dynamic environments.

Audio has always been a powerful resource for biometric authentication: thus, numerous AI-based audio authentication systems (classifiers) have been proposed. While these classifiers are effective in identifying legitimate human-generated input their security, to the best of our knowledge, has not been explored thoroughly when confronted with advanced attacks that leverage AI-generated deepfake audio. This issue presents a serious concern regarding the security of these classifiers because, e.g., samples generated using adversarial attacks might fool such classifiers, resulting in incorrect classification. In this study, we prove the point: we demonstrate that state-of-the-art audio deepfake classifiers are vulnerable to adversarial attacks. In particular, we design two adversarial attacks on a state-of-the-art audio-deepfake classifier, i.e., the Deep4SNet classification model, which achieves 98.5% accuracy in detecting fake audio samples. The designed adversarial attacks1 leverage a generative adversarial network architecture and reduce the detector’s accuracy to nearly 0%. In particular, under graybox attack scenarios, we demonstrate that when starting from random noise, we can reduce the accuracy of the state-of-the-art detector from 98.5% to only 0.08%. To mitigate the effect of adversarial attacks on audio-deepfake detectors, we propose a highly generalizable, lightweight, simple, and effective add-on defense mechanism that can be implemented in any audio-deepfake detector. Finally, we discuss promising research directions.

A plethora of contact tracing apps have been developed and deployed in several countries around the world in the battle against Covid-19. However, people are rightfully concerned about the security and privacy risks of such applications. To address these issues, in this paper we provide two main contributions. First, we present an in-depth analysis of the security and privacy characteristics of the most prominent contact tracing protocols, under both passive and active adversaries. The results of our study indicate that all protocols are vulnerable to a variety of attacks, mainly due to the deterministic nature of the underlying cryptographic protocols. Our second contribution is the design and implementation of SpreadMeNot, a novel contact tracing protocol that can defend against most passive and active attacks, thus providing strong (provable) security and privacy guarantees that are necessary for such a sensitive application. Our detailed analysis, both formal and experimental, shows that SpreadMeNot satisfies security, privacy, and performance requirements, hence being an ideal candidate for building a contact tracing solution that can be adopted by the majority of the general public, as well as to serve as an open-source reference for further developments in the field.

Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against a suspects’ database. However, existing approaches are very expensive in terms of computation and communication costs and, as a result, they do not scale well for ubiquitous deployment. To this end, we propose a general framework for privacy-preserving identification that operates by storing an encrypted version of the suspects’ database at the video cameras. We show that this approach (i) reduces the protocol to a single round of communication between the camera and the server and (ii) speeds up the computation times significantly through the use of input-independent precomputations. We apply our framework to two practical use-cases, namely, face and license plate number recognition. In addition to the identification result, our face recognition protocol discloses some trivial information to the database server; however, this information is not sufficient for the server to infer any meaningful characteristics about the underlying individuals. On the other hand, the license plate recognition protocol is provably secure and can also handle minor character recognition errors that often occur in such systems. We implemented working prototypes of both surveillance systems and our experimental results are very promising. In the case of face recognition, and for a database of 100 suspects, the online computation time at the camera and the server is 155 ms and 34 ms, respectively, while the online communication cost is only 12 KB. Similarly, for a database of 3000 entries, license plate recognition requires only 232 ms and 75 ms at the camera and the server, respectively, while the online communication cost is 375 KB.

Intelligent transportation systems necessitate a fine-grained and accurate estimation of vehicular traffic flows across critical paths of the underlying road network. However, such statistics should be collected in a manner that does not disclose the trajectories of individual users. To this end, we introduce a privacy-preserving protocol that leverages roadside units (RSUs) to communicate with the passing vehicles, in order to construct encrypted Bloom filters stemming from random vehicle IDs that are chosen secretly by the individual vehicles. Each Bloom filter represents the set of vehicle IDs that contacted the RSU but may also be used to estimate the traffic flow between any number of RSUs. More precisely, we designed a probabilistic model that approximates multipoint traffic flows by estimating the number of common vehicles among a given set of RSUs. Through extensive simulation experiments, we demonstrate that our protocol is very accurate—with a minor deviation from the real traffic flow—and show that it reduces the estimation error by a large factor, when compared to the current state-of-the-art approaches. Furthermore, our implementation of the underlying cryptographic primitives illustrates the feasibility, practicality, and scalability of the system.

Future intelligent transportation systems necessitate a fine-grained and accurate estimation of vehicular traffic flows across critical paths of the underlying road network. This task is relatively trivial if we are able to collect detailed trajectories from every moving vehicle throughout the day. Nevertheless, this approach compromises the location privacy of the vehicles and may be used to build accurate profiles of the corresponding individuals. To this end, this work introduces a privacy-preserving protocol that leverages roadside units (RSUs) to communicate with the passing vehicles, in order to construct encrypted Bloom filters stemming from the vehicle IDs. The aggregate Bloom filters are encrypted with a threshold cryptosystem and can only be decrypted by the transportation authority in collaboration with multiple trusted entities. As a result, the individual communications between the vehicles and the RSUs remain secret. The decrypted Bloom filters reveal the aggregate traffic information at each RSU, but may also serve as a means to compute an approximation of the traffic flow between any pair of RSUs, by simply estimating the number of common vehicles in their respective Bloom filters. We performed extensive simulation experiments with various configuration parameters and demonstrate that our protocol reduces the estimation error considerably when compared to the current state-of-the-art approaches. Furthermore, our implementation of the underlying cryptographic primitives illustrates the feasibility, practicality, and scalability of the system.

The proliferation of renewable energy systems and high-capacity batteries has enabled customers to trade their excess energy on the market in a peer-to-peer manner through the smart grid. At the same time, electric vehicles (EVs) are enjoying widespread acceptance, leading to a higher demand for charging stations. In this paper, we propose a system where energy traders and EV owners collectively work to satisfy the energy demands of EVs. Specifically, energy traders make bids to EV owners who, in turn, reserve their preferred charging station for a specific period of time. To protect the privacy of EV owners, we also introduce an anonymous payment system that cannot link individual owners to specific charging locations. Finally, to guarantee the security and transparency of the entire system, we store all transactions on a consortium blockchain that is managed by the energy traders and the financial institutions that support the anonymous payment system. Our experimental results indicate that the overhead of the cryptographic operations involved in the major transactions is low, in terms of both computational and communication cost.

Smart home technology is gaining popularity among end-users, as it allows them to remotely control a variety of devices in their homes. Such systems have obvious benefits in terms of automation, but at the same time pose significant threats to home owners if the underlying communications are not secure. To this end, we introduce a novel security protocol that simplifies the pairwise authentication and key exchange among smart home devices. The protocol leverages identity-based cryptography (IBC), thus relaxing the requirement for storing and managing public key certificates. Furthermore, to mitigate the risks associated with centralized key generation in IBC, we opt to generate the private keys in a distributed manner, involving all smart devices. We implemented our protocol on Raspberry Pi 3 devices and demonstrate its efficiency in terms of both computational and communication cost.

Online Social Networks (OSNs), such as Facebook and Twitter, are popular platforms that enable users to interact and socialize through their networked devices. The social nature of such applications encourages users to share a great amount of personal data with other users and the OSN service providers, including pictures, personal views, location check-ins, etc. Nevertheless, recent data leaks on major online platforms demonstrate the ineffectiveness of the access control mechanisms that are implemented by the service providers, and has led to an increased demand for provably secure privacy controls. To this end, we introduce Hide In The Crowd (HITC), a flexible system that leverages encryption-based access control, where users can assign arbitrary decryption privileges to every data object that is posted on the OSN platforms. The decryption privileges can be assigned on the finest granularity level, for example, to a hand-picked group of users. HITC is designed as a browser extension and can be integrated to any existing OSN platform without the need for a third-party server. We describe our prototype implementation of HITC over Twitter and evaluate its performance and scalability.

We propose a density-based model to detect communities of users in geo-social networks that are both socially and spatially cohesive. After formally defining the model and the geo-social distance measure it relies on, we present an algorithm that correctly identifies the underlying communities. We assess the effectiveness of our method using novel quantitative measures on the quality of the discovered communities. We also perform a visual evaluation of the discovered communities, using both real and synthetic datasets. Our results show that the proposed model produces geo-social communities with strong social and spatial cohesiveness, which can not be captured by existing graph or spatial clustering methods.

Security and anonymity are vital components in today's networked world, and play critical roles in several reallife situations, such as whistleblowing, intelligence operations, oppressive governments, etc. In this paper, we study anonymous communications in the context of Delay Tolerant Networks (DTNs). Existing work in this area relies on the standard onion routing paradigm to provide anonymity and is, therefore, vulnerable to malicious nodes. To this end, we introduce a novel message forwarding algorithm that utilizes random walks to deliver messages to their destinations. By removing the requirement to list all the intermediate nodes on the end-to-end path, our method enhances considerably the anonymity of the underlying communications. Our simulation results show that the proposed forwarding algorithm achieves high message delivery rates, at the expense of a moderate computational overhead at the mobile devices.

Dynamic spectrum access (DSA) is envisioned as a promising framework for addressing the spectrum shortage caused by the rapid growth of connected wireless devices. In contrast to the legacy fixed spectrum allocation policies, DSA allows license-exempt users to access the licensed spectrum bands when not in use by their respective owners. More specifically, in the database-driven DSA model, mobile users issue location-based queries to a white-space database, in order to identify idle channels in their area. To preserve location privacy, existing solutions suggest the use of private information retrieval (PIR) protocols when querying the database. Nevertheless, these methods are not communication efficient and fail to take into account user mobility. In this paper, we address these shortcomings and propose an efficient privacy-preserving protocol based on the Hilbert space filling curve. We provide optimizations for mobile users that require privacy on-the-fly and users that have full a priori knowledge of their trajectory. Through experimentation with two real life datasets, we show that, compared to the current state-of-the-art protocol, our methods reduce the query response time at the mobile clients by a large factor.

In the database-driven DSA model, clients learn their geographic location through a GPS device and use this location to retrieve a list of available channels from a centralized white-space database. To mitigate the potential privacy threats associated with location-based queries, existing work has proposed the use of private information retrieval (PIR) protocols when querying the database. Nevertheless, PIR protocols are very expensive and may lead to significant costs for highly mobile clients. In this paper, we propose a novel method that allows wireless users to collaborate in a peer-to-peer (P2P) manner, in order to share their cached channel availability information that is obtained from previous queries. Our experimental results with a real-life dataset show that our methods reduce the number of PIR queries by 50% to 60%, while incurring low computational and communication costs.

With the popularity of location-based services and the abundant usage of smart phones and GPS-enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Meanwhile, the fast arising trend of cloud storage and cloud computing services has provided a flexible and cost-effective platform for hosting data from businesses and individuals, further enabling many location-based applications. Nevertheless, in this database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VN-Auth, which allows a client to verify the correctness and completeness of the result set. Our approach is based on neighborhood information derived from the Voronoi diagram of the underlying spatial data set and can handle fundamental spatial query types, such as k nearest neighbor and range queries, as well as more advanced query types like reverse k nearest neighbor, aggregate nearest neighbor, and spatial skyline. We evaluated VN-Auth based on real-world data sets using mobile devices (Google Droid smart phones with Android OS) as query clients. Compared to the current state-of-the-art approaches (i.e., methods based on Merkle Hash Trees), our experiments show that VN-Auth produces significantly smaller verification objects and is more computationally efficient, especially for queries with low selectivity.

Computational Private Information Retrieval (cPIR) protocols allow a client to retrieve one bit from a database, without the server inferring any information about the queried bit. These protocols are too costly in practice because they invoke complex arithmetic operations for every bit of the database. In this paper, we present pCloud, a distributed system that constitutes the first attempt toward practical cPIR. Our approach assumes a disk-based architecture that retrieves one page with a single query. Using a striping technique, we distribute the database to a number of cooperative peers, and leverage their computational resources to process cPIR queries in parallel. We implemented pCloud on the PlanetLab network, and experimented extensively with several system parameters. Our results indicate that pCloud reduces considerably the query response time compared to the traditional client/server model, and has a very low communication overhead. Additionally, it scales well with an increasing number of peers, achieving a linear speedup.

With the popularity of location-based services and the abundant usage of smart phones and GPS enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Nevertheless, in the database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VN-Auth, that allows a client to verify the correctness and completeness of the result set. Our approach can handle both k nearest neighbor (kNN) and range queries, and is based on neighborhood information derived by the Voronoi diagram of the underlying spatial dataset. Specifically, upon receiving a query result, the client can verify its integrity by examining the signatures and exploring the neighborhood of every object in the result set. Compared to the current state-of-the-art approaches (i.e., methods based on Merkle hash trees), VN-Auth produces significantly smaller verification objects (VO) and is more computationally efficient, especially for queries with low selectivity.

The tremendous growth of the Internet has significantly reduced the cost of obtaining and sharing information about individuals, raising many concerns about user privacy. Spatial queries pose an additional threat to privacy because the location of a query may be sufficient to reveal sensitive information about the querier. In this paper we focus on k nearest neighbor ( k NN) queries and define the notion of strong location privacy , which renders a query indistinguishable from any location in the data space. We argue that previous work fails to support this property for arbitrary k NN search. Towards this end, we introduce methods that offer strong location privacy, by integrating private information retrieval (PIR) functionality. Specifically, we employ secure hardware-aided PIR, which has been proven very efficient and is currently considered as a practical mechanism for PIR. Initially, we devise a benchmark solution building upon an existing PIR-based technique. Subsequently, we identify its drawbacks and present a novel scheme called AHG to tackle them. Finally, we demonstrate the performance superiority of AHG over our competitor, and its viability in applications demanding the highest level of privacy.

Wireless data broadcast is a promising technique for information dissemination that leverages the computational capabilities of the mobile devices in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may then tune in the broadcast channel and process their queries locally without contacting the server. Previous work on spatial query processing for wireless broadcast systems has only considered snapshot queries over static data. In this paper, we propose an air indexing framework that 1) outperforms the existing (i.e., snapshot) techniques in terms of energy consumption while achieving low access latency and 2) constitutes the first method supporting efficient processing of continuous spatial queries over moving objects.

Broadcast-based peer-to-peer (P2P) networks, including flat (for example, Gnutella) and two-layer superpeer implementations (for example, Kazaa), are extremely popular nowadays due to their simplicity, ease of deployment, and versatility. The unstructured network topology, however, contains many cyclic paths, which introduce numerous duplicate messages in the system. Although such messages can be identified and ignored, they still consume a large proportion of the bandwidth and other resources, causing bottlenecks in the entire network. In this paper, we describe the distributed cycle minimization protocol (DCMP), a dynamic fully decentralized protocol that significantly reduces the duplicate messages by eliminating unnecessary cycles. As queries are transmitted through the peers, DCMP identifies the problematic paths and attempts to break the cycles while maintaining the connectivity of the network. In order to preserve the fault resilience and load balancing properties of unstructured P2P systems, DCMP avoids creating a hierarchical organization. Instead, it applies cycle elimination symmetrically around some powerful peers to keep the average path length small. The overall structure is constructed fast with very low overhead. With the information collected during this process, distributed maintenance is performed efficiently even if peers quit the system without notification. The experimental results from our simulator and the prototype implementation on PlanetLab confirm that DCMP significantly improves the scalability of unstructured P2P systems without sacrificing their desirable properties. Moreover, due to its simplicity, DCMP can be easily implemented in various existing P2P systems and is orthogonal to the search algorithms.

We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is "a high temperature reading in the vicinity of at least four high-pressure readings". We devise acquisitional and distributed protocols for evaluating this class of queries, aiming at the minimization of energy consumption. Cases of simple and complex join queries with single or multi-hop distance constraints are considered. Finally, we experimentally compare the effectiveness of the proposed solutions on an experimental platform that simulates real sensor networks. Our results show that acquisitional protocols perform best for multi-hop or high-selectivity queries while distributed techniques should be applied for the remaining cases.

PDAs, cellular phones and other mobile devices are now capable of supporting complex data manipulation operations. Here, we focus on ad-hoc spatial joins of datasets residing in multiple non-cooperative servers. Assuming that there is no mediator available, the spatial joins must be evaluated on the mobile device. Contrary to common applications that consider the cost at the server side, our main issue is the minimization of the transferred data, while meeting the resource constraints of the device. We show that existing methods, based on partitioning and pruning, are inadequate in many realistic situations. Then, we present novel algorithms that estimate the data distribution before deciding the physical operator independently for each partition. Our experiments with a prototype implementation on a WiFi-enabled PDA, suggest that the proposed methods outperform the competitors in terms of efficiency and applicability

Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.

Server selection is an important function in any replication-based infrastructure, aiming at redirecting client requests to the "best" server according to some predefined metrics. Previous research work has mainly focused on client-side redirection schemes, where the client is responsible for the server selection process. Furthermore, previous work has shown that client probing techniques perform significantly better in discovering the "best" server, compared to hop- or RTT-based schemes. Client probing, however, is not very scalable, since the number of clients and servers in the network will be very large. In this paper, we propose a novel technique to transform the server selection problem into a problem of optimal routing, which enables us to shift the redirection process from the client to the server-side. In particular, we consider the environment of a content distribution network (CDN), and propose a flexible framework that can be used to optimize the server selection process, according to various metrics and/or policies. Using trace-driven simulations, we show that the proposed method can improve significantly the response time of HTTP requests while keeping the control overhead at a very low level.

Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naive solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations.

The differentiated services (DiffServ) architecture has been proposed as a scalable solution for delivering end-to-end quality of service (QoS) guarantees over the Internet. While the scalability of the data plane emerges from the definition of only a small number of different service classes, the issue of a scalable control plane is still an open research problem. The initial proposal was to use a centralized agent, called bandwidth broker (BB), to manage the resources within each DiffServ domain and make local admission control decisions. We propose an alternative distributed approach, where the local admission decisions are made independently at the edge routers of each domain. We show, through simulation results, that this distributed approach can manage the network resources very efficiently, leading to lower bandwidth blocking rates when compared to traditional shortest path admission control. Moreover, its simplicity and distributed implementation make it a very scalable solution for resource management in DiffServ networks.

During the past few years, new types of Internet applications which require performance beyond the best-effort service that is provided by the current Internet have emerged. These applications include the transmission of voice and video, which require a fixed end-to-end delay bound in order for the end-user to perceive an acceptable level of service quality. The differentiated services (DiffServ) model has been proposed to enhance the traditional best-effort service, and provide certain quality of service (QoS) guarantees to these applications. Its current definition, however, does not allow for a high level of flexibility or assurance and, therefore, it can not be widely deployed. We introduce a new protocol for a DiffServ architecture which provides a simple and efficient solution to the above problem. It is a complete protocol, in the sense that it deals with the issues of packet scheduling, admission control, and congestion control. We show, through experimental results, that our proposed protocol can improve the flexibility and assurance provided by current solutions, while maintaining a high level of network utilization.

Video prefetching has been proposed previously for the transmission of variable-bit-rate (VBR) video over a packet-switched network. The objective of these protocols is to prefetch future frames to be stored at the customer's set-top box (STB) in periods of low link utilization. Experimental results have shown that video prefetching is very effective and it achieves much higher network utilization (i.e. larger number of simultaneous connections) than the traditional video smoothing schemes. Video prefetching, however can only be efficiently implemented when there is one centralized server that serves the different customers over a common link. In a distributed environment there is a large degradation in its performance. In this paper we introduce a new scheme that utilizes smoothing along with prefetching, to overcome the problem of distributed prefetching. We show that our scheme performs almost as well as the centralized prefetching protocol even though it is implemented in a distributed environment.