Location Privacy Preservation Research Articles

DNF-BLPP: An Effective Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal Crowdsourcing

DNF-BLPP: An Effective Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving Scheme for Service in Spatiotemporal Crowdsourcing

Q-BLPP: A Quality-Enabled Bilateral Location Privacy-Preserving Service Construction Scheme in Mobile Crowd Sensing

Q-BLPP: A Quality-Enabled Bilateral Location Privacy-Preserving Service Construction Scheme in Mobile Crowd Sensing

Achieving Privacy-Preserving Location Management in LEO-Satellite Integrated Vehicular Network with Dense Ground Stations

Achieving Privacy-Preserving Location Management in LEO-Satellite Integrated Vehicular Network with Dense Ground Stations

Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing

Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user’s actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state–action pairs by conducting a risk assessment for each state–action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.

A K-Anonymous Location Privacy-Preserving Scheme for Mobile Terminals

Mobile terminals boost the prosperity of location-based service (LBS) which have already involved in every aspect of People's daily life and are increasingly used in various industries. Aimed at solving the security and efficiency problem in the existing location privacy protection schemes, a K-anonymity location privacy preservation scheme based on mobile terminal is proposed. Firstly, number of rational dummy locations is selected from the cloaking region, from which more favorable locations are further filtered according to location entropy, so a better anonymity effect can be achieved. Secondly, the secure and efficient m-out-of-n oblivious transfer protocol is adopted, which not only avoids the dependency on the trusted anonymity center in existing schemes to improve the efficiency, but also meets the requirements for querying multiple interest points at one time. Security analyses demonstrate that this scheme satisfies such security properties as anonymity, non-forgeability and resistance to replay attack, and simulation results show that this scheme has higher execution efficiency and privacy level, while is low in communications costs.

Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation

Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method.

Privacy-Preserving Distributed Iterative Localization for Wireless Sensor Networks

Effectively determining the accurate locations of sensor nodes while preserving data privacy is an interesting and challenging problem in the research of wireless sensor networks. On the basis of the well-performing distributed iterative localization framework in wireless sensor networks, this paper proposes a dual privacy-preserving scheme based on node-intermittent updates and noises injection. A comprehensive analysis of the localization algorithm in terms of convergence and privacy-preserving performance is presented. It is theoretically demonstrated that the localization algorithm can achieve global accurate localization without leaking the privacy information of sensor nodes. At last, the performance of the privacy-preserving localization algorithm is verified by building an experimental platform containing six robots.

Energy Optimization Using Reptile Search in Wireless Sensor Networks

Energy saving in wireless sensor networks (WSNs) is a critical problem for diversity of applications. Data aggregation between sensor nodes is huge unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered an effective solution to this problem. Each cluster should have a controller denoted as a cluster head (CH) and a number of nodes located within its supervision area. Clustering demonstrated an effective result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSNs to make efficient use of the available energy sources and reducing the traffic transmission can be achieved. In solving this problem we need to find the optimal distribution of sensors and CHs; thus, we can increase the network lifetime while minimizing the energy consumption. In this paper, a Reptile Search Algorithm (RSA) for preserving location privacy and congestion avoidance with less delay guaranteed is proposed. With this routing technique, the complete sensor field is divided into different subdivisions and each subdivision elects a target area by computing its transmission distance. The backbone of the dynamic routing protocol consists of a virtual ring called bell nodes and a radial line called tentacle nodes employs more nodes to construct the network. The amount of radial line and radius of the virtual ring in a network are conjointly determined to ease the communication path from the node to sink. The radial line paths are routed directionally and bell nodes are routed with angular directions probabilistically. From the routing path, the tentacle nodes collect the data to dynamic sink which will assure that the information is going to be collected with less delay and attacker cannot guess their positions. The experimental results show that the proposed RSA method accomplishes enhanced performance in terms of energy consumption, packet delivery delay and lifetime.

Navigating the depths: a stratification-aware coarse-to-fine received signal strength-based localization for internet of underwater things

Underwater wireless sensor networks (UWSNs) are the primary enabling technology for the Internet of underwater things (IoUT), with which all underwater objects can interact and communicate. In UWSNs, localization is vital for military or civilized applications since data collected without location are meaningless. However, accurate localization using acoustic signals in UWSNs is challenging, especially for received signal strength (RSS)-based techniques. The adverse effect of hybrid loss (path and absorption loss) and stratified propagation may severely impact localization accuracy. Even though some schemes have been proposed in the literature, the accuracy is unsatisfactory. To this end, this study proposes a coarse-to-fine localization method (CFLM). The problem is reformed into an alternating nonnegative constrained least squares (ANCLS) framework, where a constrained ellipse adjustment (CEA) using block principal pivoting is proposed to obtain the coarse estimation. A refined step using a Taylor series expansion is then further presented, in which a corrected solution is acquired by iteration. Additionally, this study derives the Cramér-Rao lower bound (CRLB) to evaluate the proposed method. Simulation results show that the proposed CFLM improves the localization accuracy by up to 66 percent compared with weighted least squares (WLS), privacy-preserving localization (PPSL), two-step linearization localization approach (TLLA), particle swarm optimization-based (PSO) localization, and differential evolution-based (DE) localization under different scenarios.

Synthesizing differentially private location traces including co-locations

Privacy-preserving location synthesizers have been widely studied to perform private geo-data analysis. They have also been used for generating datasets for research or competitions. However, existing location synthesizers do not take into account the friendship information of users. Because friends tend to visit the same place at the same time in practice, a location synthesizer should consider such co-locations of friends to generate a more realistic dataset. In this paper, we propose a novel location synthesizer that generates location traces including co-locations of friends. Our location synthesizer models the information about the co-locations with two parameters: friendship probability and co-location count matrix. Our synthesizer generates a synthetic graph based on the friendship probability and then generates synthetic co-locations using the synthetic graph and the co-location count matrix. The two parameters in our synthesizer provide strong privacy guarantees—the friendship probability provides node differential privacy (DP) and the co-location count matrix provides user-level DP. We evaluate our synthesizer using two real datasets. Our experimental results show that our synthesizer preserves co-locations and other statistical features while providing DP with reasonable privacy budgets, e.g., 0.2-node DP and 2-user-level DP.

Active Oblivious Transfer-Based Location Privacy Preservation Crowdsensing Scheme

As a special type of location-based service (LBS), crowdsensing becomes more prosperous in people’s daily life. However, during the process of task distribution, the publisher’s and workers’ locations will be revealed to each other, and then their personal privacy is violated. So in this paper, in order to cope with the violation of location privacy in crowdsensing and provide privacy preservation service for both entities, an active oblivious transfer-based location privacy preservation crowdsensing scheme (short for AOTC) has been proposed. In this scheme, the oblivious transfer is used to encrypt the range of sensing grid of workers, and then matching sensing grids with the sensing region of the publisher without decryption. During the whole process, the process of location matching and results sending is disposed of by the entity of workers actively, so does not establish any data aggregation that can be used as the point of attack. As a result, the AOTC can guarantee the personal privacy of both entities in crowdsensing cannot be obtained by each other, and guarantee other workers also difficult to obtain the precise location of any workers. In addition, as workers send the sensing result to the publisher actively this scheme can also increase the probability of workers’ participation potentially. At last, the theoretical privacy preservation ability of AOTC is analyzed in the section on security analysis with three types of privacy threats. Then the performance of AOTC is compared with other similar schemes in both privacy preservation and execution efficiency, so in simulation experiments, comparison results with brief analyses will confirm that the AOTC has achieved the desired effect and will further demonstrate the superiority.

Security Compliant and Cooperative Pseudonyms Swapping for Location Privacy Preservation in VANETs

Vehicular Ad-hoc Networks (VANETs) are an essential part of the Intelligent Transportation System (ITS). VANETs promise to offer drivers and passengers safety, traffic efficiency, and infotainment services. To this end, VANETs require vehicles to periodically broadcast unencrypted beacon messages that contain position, velocity, timestamp, and identity. Consequently, VANETs naturally expose users' identities and locations, facilitating tracking by an adversary. To address VANETs location privacy issues, researchers have proposed different pseudonym-based privacy-preserving techniques in the literature. One of the techniques is pseudonym swap. In pseudonym swap techniques, vehicles exchange pseudonyms with each other hence fewer pseudonyms for each vehicle since vehicles can reuse pseudonyms. However, existing pseudonym swap-based schemes either exchange pseudonyms via Roadside Units (RSUs) or ignore VANETs' security requirements by not informing the Certification Authority of the exchanges. Hence, they suffer from high RSUs deployment costs, inflexibility to location privacy preservation, unaccountability, and repudiation attacks. Moreover, the lower level of location privacy provided by the schemes demands further improvements. Therefore, we propose a novel pseudonym swap scheme that uses swappable and non-swappable pseudonyms to attain infrastructure independence while adhering to VANET security requirements. In this scheme, vehicles, independent of RSUs, cooperatively swap and change pseudonyms within mix-contexts having a high level of location privacy. Each exchange is then reported to the authority to enforce accountability and non-repudiation. Besides adhering to VANET security requirements, the simulation experiments show that our scheme outperforms existing pseudonym swap schemes, providing VANET users with a high level of location privacy.

Towards accurate and privacy-preserving localization using anchor quality assessment in Internet of Things

Crowdsourced localization plays a significant role for the applications in Internet of Things. Even though existing studies have proposed privacy-preserving localization algorithms to protect location information from being disclosed to untrusted parties, they still need to consider improving the accuracy of the algorithms so as to enable practical localization services. In this paper, by extending the research scope of previous literature, we investigate a novel localization problem of achieving both accuracy improvements and privacy preservation from the perspective of anchor quality assessments. Specifically, the quality of anchor (QoA) is proposed to quantitatively describe the capability of anchor users to provide accurate data by taking into account factors such as residual energy, localization distance, and localization angle. Based on efficient and privacy-preserving maximum and minimum computation techniques, a privacy-preserving QoA assessment method is proposed. Besides, an accurate and privacy-preserving localization (APPL) algorithm is developed by using the privacy-preserving assessment method. The APPL algorithm selects anchor users who can provide accurate data to participate in localization based on the results of the QoA assessment. It is proved that the APPL algorithm is correct, accurate, privacy-preserving and efficient by theoretical analyses. Both experiments and simulations are conducted to evaluate the performance of the APPL algorithm and validate the analytical results.

RFPM: A RSU‐aided framework for pseudonym management to preserve location privacy in IoV

AbstractThe Internet of Vehicles (IoV) has become a rapidly growing research area, with the USA, EU, and Japan leading the way. IoV safety applications rely on broadcasting messages to communicate with peers and infrastructure. However, these messages contain sensitive information that can be used by an adversary to track vehicles' movements or cause chaos if not properly protected, resulting in location privacy concerns. Despite various schemes being proposed to address this issue, a standard approach to providing proper privacy to IoV users has yet to be established. This article proposes a roadside unit (RSU)‐aided framework for pseudonym management to preserve location privacy in IoV by enabling anonymity. The RSUs function as key entities, collecting, distributing, and shuffling pseudonyms for use by vehicles to enforce anonymity. The proposed framework has been evaluated through experimental simulations on PREXT, demonstrating better performance than existing schemes in terms of privacy metrics, including traceability, anonymity set, confusion matrix, and maximum entropy. Realistic terrain has been taken into account, and the framework is designed to be flexible and adaptable for future needs.

A Caching-Based Dual K-Anonymous Location Privacy-Preserving Scheme for Edge Computing

Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes a challenge. Many existing schemes fail to leverage the responses from a LBS server; such responses can be cached to answer subsequent queries. As a result, providing users with a relatively comprehensive level of location privacy protection is troublesome. In this article, we propose a caching-based dual K-anonymous (CDKA) location privacy preserving scheme in edge computing environments. Our scheme employs an edge server to intercedes between a user and LBS the server. We reduce the load on the user device by applying multi-level caching and to protect location privacy through dual anonymity. To ensure the location privacy in our construction, we set mobile clients and edge servers as anonymous. We use caching to lower the communication overhead and enforce the location privacy. The security analysis of our scheme supports its robustness against the edge server and the LBS server privacy offenses. We rely on the computation time, the communication cost and the cache hit ratio to evaluate our work against existing constructions. The results are two-fold: our work possesses a better response rate down to 15 ms to 32.6 ms and exhibits lower communication cost requirements down to 6.2 and 38.9 KB compared to existing works. Our scheme witnesses a higher cache hit ratio up to 13.6% and 39.1% compared to the literature.

Comparative Analysis of Privacy Preserving Location Based Services Mechanisms

Comparative Analysis of Privacy Preserving Location Based Services Mechanisms

Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based Techniques

The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker's ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker's ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks.

A Novel Approach for Location Privacy Preservation of Clusters

Location privacy is one of the burning issues in respect of the LBS and other services, since there are several risks of data leakage happening all over the world as well as the location data leakage is also happening. Withthe increment of these data breaches several advertisements and bad cyber crime happens with different person. To solve these issues and to secure the location privacy as well as location data hiding we have applied k- anonymity algorithm to anonymise the data sets so that the location information of users will hide themselves. The aim of our paper is to demonstrate the novel approach for location privacy preservation using machine learning and deep learning algorithms applicable on various parameters as well as on various data sets. We have applied L diversity and T closeness for the same so that we make sure about the protection of the individual stored data dets as well at the same time.

PPTA: A location privacy-preserving and flexible task assignment service for spatial crowdsourcing

With the rapid growth of sensor-rich mobile devices, spatial crowdsourcing (SC) has emerged as a new crowdsourcing paradigm harnessing the crowd to perform location-dependent tasks. To appropriately select workers that are near the tasks, SC systems need to perform location-based task assignment, which requires collecting worker locations and task locations. Such practice, however, may easily compromise the location privacy of workers. In light of this, in this paper, we design, implement, and evaluate PPTA, a new system framework for location privacy-preserving task assignment in SC with strong security guarantees. PPTA takes advantage of only lightweight cryptography (such as additive secret sharing, function secret sharing, and secure shuffle), and provides a suite of tailored secure components required by practical location-based task assignment processes. Specifically, aiming for practical usability, PPTA is designed to flexibly support two realistic task assignment settings: (i) the online setting where tasks arrive and get processed at the SC platform one by one, and (ii) the batch-based setting where tasks arrive and get processed in a batch. Extensive experiments over a real-world dataset demonstrate that while providing strong security guarantees, PPTA supports task assignment with efficacy comparable to plaintext baselines and with promising performance.

A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing.

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary's exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy "Give up a Pawn to Save a Chariot". Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use.