Location Privacy Protection Algorithm Research Articles

Location Privacy Protection for the Internet of Things with Edge Computing Based on Clustering K-Anonymity.

With the development of the Internet of Things (IoT) and edge computing, more and more devices, such as sensor nodes and intelligent automated guided vehicles (AGVs), can serve as edge devices to provide Location-Based Services (LBS) through the IoT. As the number of applications increases, there is an abundance of sensitive information in the communication process, pushing the focus of privacy protection towards the communication process and edge devices. The challenge lies in the fact that most traditional location privacy protection algorithms are not suited for the IoT with edge computing, as they primarily focus on the security of remote servers. To enhance the capability of location privacy protection, this paper proposes a novel K-anonymity algorithm based on clustering. This novel algorithm incorporates a scheme that flexibly combines real and virtual locations based on the requirements of applications. Simulation results demonstrate that the proposed algorithm significantly improves location privacy protection for the IoT with edge computing. When compared to traditional K-anonymity algorithms, the proposed algorithm further enhances the security of location privacy by expanding the potential region in which the real node may be located, thereby limiting the effectiveness of "narrow-region" attacks.

DPSP: A Dynamic Pseudonym Swap Program Based Location Privacy Protection Algorithm for Internet of Vehicles

DPSP: A Dynamic Pseudonym Swap Program Based Location Privacy Protection Algorithm for Internet of Vehicles

A Backbone-Network-Construction-Based Multi-AUV Collaboration Source Location Privacy Protection Algorithm in UASNs

Underwater acoustic sensor networks (UASNs) are effective instruments for monitoring marine environments and surveying seabed resources, it is important to improve their security protection, including source location privacy protection. Numerous strategies have been presented by researchers to strengthen location privacy, however, the majority of these plans have expensive energy costs. Therefore, a backbone network construction-based multi-autonomous underwater vehicle (AUV) collaboration source location privacy protection (BNCSLP) algorithm has been enhanced to address this issue. First, the nodes in the network are split into various clusters, and an entire network is segmented into various regions. The backbone network is built using clusters that house the source. To prevent the adversary’s tracking, the AUV alternately chooses alternative backbone nodes and relays the source and fake data. Then, the cluster head determines whether to update the clusters by calculating how similar the data are with nearby clusters. The nearest neighbor technique is used by the updated cluster to anticipate the data and to reduce the energy utilization of forwarding the data packets, resulting in that there is less chance of data packets being intercepted and the source location being revealed. Finally, the AUV re-plans the trajectory, which shortens the AUV’s travelling path because only fewer cluster heads need to be accessed, decreasing the time it takes for data to be transmitted.

An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services.

Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with the risk of location privacy disclosure, which can infringe upon personal privacy and security. In this paper, a location privacy protection method based on differential privacy is proposed, which efficiently protects users' locations, without degrading the performance of LBS. First, a location-clustering (L-clustering) algorithm is proposed to divide the continuous locations into different clusters based on the distance and density relationships among multiple groups. Then, a differential privacy-based location privacy protection algorithm (DPLPA) is proposed to protect users' location privacy, where Laplace noise is added to the resident points and centroids within the cluster. The experimental results show that the DPLPA achieves a high level of data utility, with minimal time consumption, while effectively protecting the privacy of location information.

Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior

Location-based services have become an important part of our daily lives, and while users enjoy convenient Internet services, they also face the risk of privacy leakage. K-anonymity is a widely used method to protect location privacy, but most existing K-anonymity location privacy protection schemes use virtual locations to construct anonymity zones, which have the problem of being vulnerable to attackers through background knowledge, while the improved collaborative K-anonymity scheme does not sufficiently consider whether collaborating users share similar attributes. We propose a distributed K-anonymity location privacy-preserving algorithm based on interest points and user social behaviors to solve these problems in existing K-anonymity schemes. The method determines the similarity of users by their interest points and social behaviors and then selects users with high similarity to build an anonymous set of collaborative users. Finally, to ensure the relatively uniform distribution of collaborative users, a homogenization algorithm is used to make the anonymous location points as dispersed as possible. The experimental results showed that our algorithm can effectively resist background attacks, and the uniformly distributed anonymous location points can achieve higher-quality anonymous regions.

A Collision-Free-Transmission-Based Source Location Privacy Protection Scheme in UASNs Under Time Slot Allocation

Underwater acoustic sensor networks (UASNs) are data driven, and the data generation is infeasible without source nodes, sensors, and equipment deployed underwater. However, underwater acoustic communication between nodes is especially vulnerable to malicious attacks, which could cause the source data packets to be tracked and indirectly expose the locations of source nodes. Once the source node is located, the security of the network and the monitored object will be considered threatened. A collision-free transmission-based source location privacy protection algorithm in UASNs under time slot allocation (CFTSLP-TSA) is proposed in this article. First, we select suitable fake source nodes to generate fake data packets, aiming at concealing the traffic of the source data packets. Then, different transmission time slots are arranged for the source and the fake data packets, in order to avoid interference in the transmission between one another. In addition, a handshake-based relay node selection strategy is presented. This not only makes the paths more diverse but also requires a higher requirement for the attacker to track the flow of source packets while the source packets are transmitted without collisions. The performance of the simulation shows that the CFTSLP-TSA produces both a greater source location privacy protection level and a better data packet delivery rate compared with the other state-of-the-art schemes.

LP-BT: A location privacy protection algorithm based on ball trees

LP-BT: A location privacy protection algorithm based on ball trees

Smart-Area-Selection Based Location Privacy Enhancement

Protecting location privacy from location-based service (LBS) providers is a challenging problem. Existing mechanisms either use a trusted third party (TTP) or location perturbation techniques. In TTP-based mechanisms, when the security of a TTP is compromised, the user’s privacy is breached. On the other hand, in location perturbation-based mechanisms, privacy is achieved at the expense of query accuracy. To overcome these problems, in this article, a novel smart-area-selection (SAS)-based location privacy protection algorithm is proposed. The proposed approach does not require the user to trust any party including LBS. The proposed efficient mechanism achieves privacy without compromising the accuracy of the query. Furthermore, our SAS-based mechanism uses a user-specific geographical coordinate system and public key encryption for additional security. In the existing location privacy protection mechanisms, when LBSs are accessed continuously, more location information can be extracted by a smart adversary. A specially designed query region spreading algorithm is used in the proposed mechanism to overcome this problem. The validity of the proposed SAS-based location privacy protection mechanism is demonstrated by simulation results.

LBS Meets Blockchain: An Efficient Method With Security Preserving Trust in SAGIN

In Internet of Vehicles (IoV), the vehicle <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> network (VANET) provides the location-based service (LBS) when vehicles communicate with the dynamic environment. As an integration of satellite systems and terrestrial communications, the space–air–ground integrated network (SAGIN) provides a reliable and efficient way for LBS. But the privacy protection in SAGIN cannot meet LBS security requirements well, so we present a blockchain-based LBS security preserving trust model. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -anonymous location privacy protection algorithm is used to hide users’ real position so that users can avoid personal privacy disclosure when requesting LBSs. We propose a trust management algorithm which can detect the malicious behaviors when constructing anonymous regions and clear the malicious users out of the system. Besides, we use blockchain to implement the transparency and conditional anonymity of the system. Missive experiments indicate that our scheme is feasible and outperforms part of state-of-the-art privacy protection approaches.

Releasing Differentially Private Trajectories with Optimized Data Utility

The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.

On Location Privacy Protection Algorithm Based on Edge Computing and Caching

To resolve the communication overhead problem of anonymous users, we propose a location privacy protection method based on the cache technology. In particular, we first place the cache center on edge server nodes to reduce interaction between servers and users. In this way, the risk of privacy leaks can be reduced. Furthermore, to improve the caching hit rate, a prediction system based on Markov chain is designed to protect the trajectory privacy of mobile users. Simulations show that the algorithm can protect the privacy of users and reduce the transmission delay.

Location privacy protection of maritime mobile terminals

Mobile Edge Computing (MEC) can support various high-reliability and low-delay applications in Maritime Networks (MNs). However, security risks in computing task offloading exist. In this study, the location privacy leakage risk of Maritime Mobile Terminals (MMTs) is quantified during task offloading and relevant Location Privacy Protection (LPP) schemes of MMT are considered under two kinds of task offloading scenarios. In single-MMT and single-time offloading scenario, a dynamic cache and spatial cloaking-based LPP (DS-CLP) algorithm is proposed; and under the multi-MMTs and multi-time offloading scenario, a pseudonym and alterable silent period-based LPP (PA-SLP) strategy is proposed. Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy. Meanwhile, extending the alterable silent period, increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP. Furthermore, the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.

DPLQ: Location‐based service privacy protection scheme based on differential privacy

The existing privacy protection schemes for Location-Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial-temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k-means algorithm and l-diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k-anonymous algorithm, the users' query privacy protection algorithm builds a query k-anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k-anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users.

A Location Privacy Protection Algorithm Based on Double K-Anonymity in the Social Internet of Vehicles

As an emerging complex social network, the Social Internet of Vehicles (SIoV) potentially exposes user location privacy. In this letter, we propose a location privacy protection method based on double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -anonymity that hides user locations and request information. The cloud server is introduced as a trusted third party to isolate the direct communication between users and the service provider, while correlation between identities and requests is also reduced by means of a permutation and combination method. Extensive simulations are conducted to demonstrate that the method can protect user location privacy to the greatest extent possible while still ensuring service availability.

A location privacy protection algorithm based on differential privacy in sensor network

The continuous development and innovation in sensor networks has simplified the processes of data collection and transmission and has led to their widespread use. However, these processes often involve sensitive location information. Therefore, the location of a sensor should be protected for privacy reasons during data collection and transmission. In this paper, we propose a privacy protection scheme for location in a sensor network, based on differential privacy. Although the algorithm satisfies the privacy protection requirements, the accumulation of noise in the privacy protection process may decrease the utility of the data. To solve this problem, we propose a square sum error-based location privacy protection algorithm. Our algorithm can reduce the accumulation of noise, while also protecting the structure of the sensor network.

A MADM Location Privacy Protection Method Based on Blockchain

Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.

A location privacy protection algorithm based on differential privacy in sensor network

A location privacy protection algorithm based on differential privacy in sensor network

A Novel Location Privacy-Preserving Approach Based on Blockchain.

Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.

Optimal anonymous location privacy protection algorithm based on grid user density

ABSTRACT The existing location anonymity algorithms do not consider the distribution of user density in the region. The area of anonymous domain is not the most appropriate and the query workload is redundant. To solve this problem, this paper proposes optimal anonymous location privacy protection algorithm based on grid user density. Taking the user density of the regional grid as the core and using reasonable dynamic shrinkage and expansion rules to find the most suitable anonymous domain, and reduces the anonymous domain as much as possible to meet the user privacy parameter configuration, thereby improving LBS service quality. This paper builds simulation dataset based on road network moving objects, and simulation experiments are performed on location privacy protection method, which proves the effectiveness of this method. At the same time, the real road network floating vehicle data is selected for the application of algorithm, which proves the feasibility of this method.

Service Recommendation Middleware Based on Location Privacy Protection in VANET

With the help of location-based services (LBS), it makes driving more convience for drivers. However, because the untrusted LBS server may leak the user's location information, the user's privacy is threatened. Moreover, the existing methods of location privacy protection do not take into account the impact of context on privacy protection demand. In addition, heterogeneous data sensed by vehicles also increases the complexity of application development. In order to solve the above problems, we propose a context-based location privacy protection middleware architecture, named PP-OSGi. The middleware simplifies application development by shielding the heterogeneity of vehicle sensed data and upper-layer applications. Furthermore, in order to protect the real location information of service request vehicles under different vehicle densities in a certain area, we propose a dynamically adjustable k-anonymous (DAK) algorithm and a location privacy protection (DLP) algorithm based on a dummy location, which are all encapsulated in PP-OSGi. The DAK and DLP algorithms dynamically determine the location privacy protection strength in different contexts based on the user's location privacy preference model, select an anonymous group of neighboring vehicles to construct a anonymous area, and obtain a dummy location of the service request vehicle. The experimental results show that, under the premise of protecting the location privacy of vehicles, the success rate of service requests is improved and the communication cost between vehicles is reduced.