Related papers: A Clustering-based Location Privacy Protection Sch…
As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience…
Do people care about their location privacy while using location-based service apps? This paper aims to answer this question and several other hypotheses through a survey, and review the privacy preservation techniques. Our results indicate…
The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users' privacy. Existing privacy preserving protocols require trusted nodes, with serious security and…
Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with untrusted third parties. In this work, we study privacy-utility…
Knowledge discovery is one of the main goals of Artificial Intelligence. This Knowledge is usually stored in databases spread in different environments, being a tedious (or impossible) task to access and extract data from them. To this…
Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great…
Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client…
This paper describes a new model for sharing location info for mobile users. This approach can operate without the need for disclosing identity info to third party servers. It could be described as a safe location sharing model. The…
Location-Based Recommendation Services (LBRS) has seen an unprecedented rise in its usage in recent years. LBRS facilitates a user by recommending services based on his location and past preferences. However, leveraging such services comes…
Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is…
Clustering is an important tool for data exploration where the goal is to subdivide a data set into disjoint clusters that fit well into the underlying data structure. When dealing with sensitive data, privacy-preserving algorithms aim to…
Location privacy leaks can lead to unauthorised tracking, identity theft, and targeted attacks, compromising personal security and privacy. This study explores LLM-powered location privacy leaks associated with photo sharing on social…
Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break…
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high…
Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal…
As people's daily life becomes increasingly inseparable from various mobile electronic devices, relevant service application platforms and network operators can collect numerous individual information easily. When releasing these data for…
With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its…
Local Differential Privacy (LDP) is popularly used in practice for privacy-preserving data collection. Although existing LDP protocols offer high utility for large user populations (100,000 or more users), they perform poorly in scenarios…
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off between privacy and utility in many real-world applications.…
The burgeoning technology of Mobile Edge Computing is attracting the traditional LBS and LS to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat…