Crowdsourced traffic monitoring provides real-time insights for urban planning and congestion management, but directly reporting users’ GPS coordinates poses serious privacy risks. We propose HSR-PRR, an accuracy-enhanced frequency estimation scheme under Local Differential Privacy (LDP) for traffic monitoring. By combining grid-based discretization with hash-based binning, the server partitions the domain and filters irrelevant reports, reducing variance and improving estimation accuracy. The scheme is highly communication-efficient, requiring only a 1-bit response per reporting user. We prove that reporting users satisfy ε-LDP, while non-reporting users incur minimal leakage, and the query condition achieves k-anonymity. Analysis and experiments show that HSR-PRR achieves higher accuracy and lower total communication overhead than PRR, making it a practical and scalable solution for large-scale, privacy-preserving traffic monitoring.

Crowdsourcing has become a widely utilized method for data collection and analysis; however, privacy concerns remain a significant challenge. In this paper, we introduce a novel and efficient private set frequency (PSF) query scheme designed for crowdsourcing scenarios. Our proposed scheme is based on edge computing and leverages local differential privacy (LDP) and Bloom filter techniques to ensure both query privacy and high communication efficiency. Specifically, we employ two non-colluding edge devices to assist the server in achieving highaccuracy query result estimation while preserving the privacy of both the server's query set and users' sensitive data. A comprehensive security analysis confirms that the query value remains confidential, and users' privacy is guaranteed under$\varepsilon$-LDP. Additionally, performance evaluations demonstrate the efficiency and improved accuracy of our proposed scheme.

In crowdsourcing applications, gathering and analyzing users’ strong positive (1) or negative (-1) reactions to a large number of items is crucial for improving service quality, particularly in recommendation systems. However, protecting users’ privacy while handling diverse sparse patterns in contexts with a large dimension sizedposes significant challenges for efficient and privacy-preserving data aggregation. To address these challenges, in this paper, we propose an optimizedk-sparse vector mean estimation scheme under Local Differential Privacy (LDP), ensuring that each user’s entire set of up tokprivate values from {−1, 1} satisfies ε-LDP. Specifically, our proposed scheme employs a seed mining technique in conjunction with PRNG Randomizer, which allows users to send their data only once while enabling the server to accurately estimate any value’s mean in the domain. Our scheme achieves an asymptotically optimal error ofO( 1/ε√n), equivalent to that of a 1-sparse case, while also ensuring efficient communication costs. The communication cost remains at a minimal level ofO(1) (only 2 bytes per user’s report) for smallerkvalues and scales toO(k) for largerk, due to efficient binning strategies. Extensive experimental results confirm that our results align with theoretical expectations, demonstrating that our scheme not only preserves user privacy but also ensures higher accuracy compared to other schemes.

Crowdsourcing has become a widely used method for data collection and analysis, yet its privacy remains a challenge. In this paper, we present a new efficient and privacy-preserving conjunctive query scheme for crowdsourcing scenarios. The scheme employs the Local Differential Privacy (LDP) technique to ensure both query privacy and high communication efficiency. Specifically, when an aggregator launches a conjunctive query to a set of crowdsourcing users, the query condition will not be leaked. To respond the query, each user just needs to return one bit back to the aggregator. By integrating prefix encoding technique, our proposed scheme can also efficiently support conjunctive queries with one range query condition. Detailed security analysis shows our proposed scheme can achieve desirable security requirements. In addition, performance evaluations also indicate its efficiency. Furthermore, extensive experiments demonstrate our proposed scheme can achieve high accuracy while ensuring ε-LDP.

Multi-location task allocation is one of the most crucial issues in vehicular crowdsourcing (VCS). To ensure service quality, the VCS service provider prefers to assign multi-location tasks to the workers whose future trajectories have high spatial proximity with the task locations. However, this process requires workers and task owners to upload their precise locations to a not-fully-trusted service provider, thereby raising location privacy concerns. Although several privacy-preserving trajectory similarity evaluation schemes have been proposed, they either fail to match the multi-location task allocation scenario, or incur nontrivial computational costs due to homomorphic encryption. To address these challenges, we propose a novel efficient privacy-preserving multi-location task allocation scheme in fog-assisted VCS. Specifically, we design a lightweight secure Euclidean distance computation protocol based on arithmetic secret sharing (ASS), which can compute Euclidean distance without revealing the two input locations. Then, based on this protocol, we build our scheme that supports multi-location task allocation based on Hausdorff semi-distance (HSD). Our security analysis demonstrates the location privacy preservation of our scheme, and the experiment results on a real dataset also validate the efficiency of our scheme.

Crowdsourcing has received considerable attention in recent years; however, privacy in crowdsourcing remains a challenge. In this paper, we present a privacy-preserving range sum query scheme under Local Differential Privacy (LDP) that not only enhances accuracy but also guarantees privacy in crowdsourcing applications. Specifically, our proposed scheme employs keyed hash, prefix encoding, and garbled bloom filter techniques to convert a large query range into a small domain, independent of the range length, thus improving accuracy. For the query response, the Optimal Unary Encoding (OUE) technique is applied to achieve ε-LDP. Security analysis shows that our proposed scheme can achieve the desirable privacy requirement for users' private items and the server's query range. In addition, performance evaluations also confirm the efficiency of our scheme in terms of computational costs and communication overhead. Furthermore, extensive experiments validate that our proposed scheme outperforms a potential strawman solution in terms of accuracy.

Vehicular crowdsensing (VCS) has emerged as a promising paradigm, in which spatio-temporal-based sensing tasks are outsourced to intelligent connected vehicles (ICVs) carrying sensor-equipped devices. A critical issue of VCS is to guarantee the spatio-temporal sensing coverage by assigning tasks to appropriate vehicles, which inevitably requires vehicles’ precise locations or trajectories and thus raises location privacy concerns. To address this problem, we propose a novel secret sharing-based efficient privacy-preserving task allocation scheme for VCS, which can select sensing vehicles with approximately optimal total spatio-temporal coverage based on their future trajectories while achieving strong location privacy preservation for users (customers and sensing vehicles). With a grid-based region encoding method, a user’s location information is encoded as a binary array, termed as the region code. Based on the idea of secret sharing, we design a bit-wise XOR-based secret splitting method to split a user’s region code into two random shares and separately transmit them to two fog servers, thereby perfectly hiding the original location information. With a carefully-designed code permutation mechanism and a greedy task allocation algorithm, the cloud server and fog servers can efficiently collaborate and complete task allocation based on permuted region codes without revealing users’ location information. Detailed security analysis shows that our proposed scheme effectively preserves users’ location privacy. Extensive experiments conducted on a realistic traffic scenario data set also demonstrate that it is efficient in communication and computation while achieving large total spatio-temporal coverage.

Crowdsourcing, which is regarded as one of the most important data collection techniques in Internet of Things (IoT) and Big Data era, has received significant attention in recent years. However, privacy concerns persist across various crowdsourcing scenarios. In this paper, aiming to address users’ privacy issues in crowdsourcing scenarios, we propose an efficient and privacy-preserving range query scheme under Local Differential Privacy (LDP) setting. Specifically, given a domain V = {0, 1, 2, ..., d – 1} where d = 2w, our proposed scheme integrates binary heap tree, prefix encoding, randomized response, and pseudo-random number generator techniques to enable each user to report only w bits as a query response, which is sufficient for a server to efficiently compute the range query result for any range [a, b] in the domain V. Security analysis demonstrates that our proposed scheme can achieve ε-LDP, effectively preserving the privacy of users’ private items. In addition to its low communication overhead, performance evaluation also indicates our proposed scheme is computationally efficient when the pre-computation is implemented at the server. Furthermore, our proposed scheme exhibits higher accuracy compared to previously reported flat and tree-based methods, especially for a large domain size d and a large range length m = b – a + 1.

As the proliferation of smart vehicles has fostered an abundance of real-time data, various data analysis tools, such as aggregation queries, are expected to be deployed to extract insights and make transportation systems much smarter. Meanwhile, to cope with the growing service scale, edge servers are employed to collect data and deliver the service, which however provokes privacy concerns related to the reported data and user queries. Previously reported solutions on privacy-preserving aggregation queries focus on static datasets or require data persistence, leading to storage pressure and slower query responses. In this paper, we propose a privacy-preserving dynamic aggregation query scheme using edge servers, specifically addressing the problem of online aggregation queries. By combining homomorphic encryption and predicate encryption, our scheme enables the edge server to aggregate real-time data and respond to queries, safeguarding sensitive information from vehicles and data users. The integration of advanced cryptographic primitives ensures data and query privacy and integrity. Comprehensive theoretical analyses demonstrate our scheme's effectiveness in privacy preservation, boasting a manageable computational and communication overhead. The scheme, thus, presents a practical solution for privacy-preserving dynamic aggregation queries, fulfilling an unmet need in real-time transportation systems.

Crowdsourcing has recently become a popular method of outsourcing tasks to many individuals in data-oriented applications. However, privacy is still a significant concern as crowdsourcing relies on individual responses. This paper focuses on range queries in crowd sourcing scenarios and proposes an efficient range query scheme, called ERQ, under Local Differential Privacy (LDP). ERQ is characterized by employing i) the accumulated encoding and perturbation techniques to protect the privacy of user data, and ii) the k-anonymity technique to conceal the real endpoints of a range query. Detailed security analysis shows that ERQ can achieve the desirable privacy requirements. In addition, performance evaluation also indicates ERQ is efficient in terms of low computational costs and communication overhead, and can achieve better accuracy while preserving privacy.

While crowdsourcing for data collection has become increasingly popular in data-driven applications, privacy remains a significant challenge. This paper presents an effective scheme for conducting range queries under local differential privacy (LDP) in crowdsourcing applications, which addresses the privacy challenges that arise in such scenarios. In particular, our proposed scheme utilizes Prefix Encoding (PE) and Bloom Filter (BF) techniques to convert a large domain into a binary domain for improved query accuracy. When responding to the query, individual users can check a Bloom filter to determine whether their private item is within the query range and use the Basic Randomized Response (BRR) technique to perturb their result for achieving ε-LDP. Detailed security analysis shows that our proposed scheme can preserve user’s item privacy and also keep an external passive attacker from learning the query range. In addition, performance evaluation shows that the proposed scheme is efficient in terms of computational cost and communication overhead, while effectively balancing range query accuracy and privacy.