25,731 research outputs found
Query Processing In Location-based Services
With the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. To address both of the scalability factors, distributed computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. In the first part of this dissertation, we propose a distributed framework to process moving monitoring queries over moving objects in a spatial network environment. In the second part of this dissertation, in order to reduce the communication cost, we leverage both on-demand data access and periodic broadcast to design a new hybrid distributed solution for moving monitoring queries in an open space environment. Location-based services make our daily life more convenient. However, to receive the services, one has to reveal his/her location and query information when issuing locationbased queries. This could lead to privacy breach if these personal information are possessed by some untrusted parties. In the third part of this dissertation, we introduce a new privacy protection measure called query l-diversity, and provide two cloaking algorithms to achieve both location kanonymity and query l-diversity to better protect user privacy. In the fourth part of this dissertation, we design a hybrid three-tier architecture to help reduce privacy exposure. In the fifth part of this dissertation, we propose to use Road Network Embedding technique to process privacy protected queries
A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services
With the rapid pervasion of location-based services (LBSs), protection of location privacy has become a significant concern. In most continuous LBSs' privacy-preserving solutions, users need to transmit the location query data to an untrusted location service provider (LSP) to obtain query results, and the users discard these results immediately after using them. This results in an ineffective use of these results by future queries and in turn leads to a higher risk to user privacy from the LSP. To address these issues, we generally use caching to cache the query results for users' future queries. However, the minimization of the interaction between users and LSPs is a challenge. In this paper, we propose an enhanced user privacy scheme through caching and spatial K-anonymity (CSKA) in continuous LBSs; it adopts multi-level caching to reduce the risk of exposure of users' information to untrusted LSPs. In continuous LBS queries, our scheme first utilizes the Markov model to predict the next query location according to the user mobility. Then, according to the predicted location, cell's cache contribution rate, and data freshness, an algorithm for forming spatial K-anonymity is designed to improve the user's cache hit rate and enhance the user location privacy. The security analysis and simulation results demonstrate that our proposed CSKA scheme can provide higher privacy protection than a few previous methods, and it can minimize the overhead of the LBS server
ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs
The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal
Rethinking Location Privacy for Unknown Mobility Behaviors
Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely
consider that users' data available for training wholly characterizes their
mobility patterns. Thus, they hardwire this information in their designs and
evaluate their privacy properties with these same data. In this paper, we aim
to understand the impact of this decision on the level of privacy these LPPMs
may offer in real life when the users' mobility data may be different from the
data used in the design phase. Our results show that, in many cases, training
data does not capture users' behavior accurately and, thus, the level of
privacy provided by the LPPM is often overestimated. To address this gap
between theory and practice, we propose to use blank-slate models for LPPM
design. Contrary to the hardwired approach, that assumes known users' behavior,
blank-slate models learn the users' behavior from the queries to the service
provider. We leverage this blank-slate approach to develop a new family of
LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we
empirically show that our proposal outperforms optimal state-of-the-art
mechanisms designed on sporadic hardwired models. On non-sporadic location
privacy scenarios, our method is only better if the usage of the location
privacy service is not continuous. It is our hope that eliminating the need to
bootstrap the mechanisms with training data and ensuring that the mechanisms
are lightweight and easy to compute help fostering the integration of location
privacy protections in deployed systems
User-centric Privacy Engineering for the Internet of Things
User privacy concerns are widely regarded as a key obstacle to the success of
modern smart cyber-physical systems. In this paper, we analyse, through an
example, some of the requirements that future data collection architectures of
these systems should implement to provide effective privacy protection for
users. Then, we give an example of how these requirements can be implemented in
a smart home scenario. Our example architecture allows the user to balance the
privacy risks with the potential benefits and take a practical decision
determining the extent of the sharing. Based on this example architecture, we
identify a number of challenges that must be addressed by future data
processing systems in order to achieve effective privacy management for smart
cyber-physical systems.Comment: 12 Page
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