Privacy-Preserving Shortest Path Computation

Navigation is one of the most popular cloud computing services. But in virtually all cloud-based navigation systems, the client must reveal her location and destination to the cloud service provider in order to learn the fastest route. In this work, we present a cryptographic protocol for navigation on city streets that provides privacy for both the client's location and the service provider's routing data. Our key ingredient is a novel method for compressing the next-hop routing matrices in networks such as city street maps. Applying our compression method to the map of Los Angeles, for example, we achieve over tenfold reduction in the representation size. In conjunction with other cryptographic techniques, this compressed representation results in an efficient protocol suitable for fully-private real-time navigation on city streets. We demonstrate the practicality of our protocol by benchmarking it on real street map data for major cities such as San Francisco and Washington, D.C.Comment: Extended version of NDSS 2016 pape

Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data

IEEE When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too time-consuming for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results

Encryption Mechanism And Resource Allocation Optimization Based On Edge Computing Environment

A method for optimizing encryption mechanism and resource allocation based on edge computing environment is proposed. A local differential privacy algorithm based on a histogram algorithm is used to protect user information during task offloading, which allows accurate preservation of user contextual information while reducing interference with the playback decision. To efficiently offload tasks and improve offloading performance, a joint optimization algorithm for task offloading and resource allocation is proposed that optimizes overall latency. A balance will be found between privacy protection and task offloading accuracy. The impact of contextual data interference on task offloading decisions is minimized while ensuring a predefined level of privacy protection. In the concrete connected vehicle example, the method distributes tasks among roadside devices and neighboring vehicles with sufficient computational resources

Continuous Spatial Query Processing in Mobile Information Systems

Nowadays, many mobile applications provide location-based services that allow users to access location-related information from anywhere, whenever they desire. A moving user can issue queries to access information about moving or static objects. Continuous spatial query processing systems are used for this type of application. We propose two query processing strategies for location based services. The objectives of our strategies are to reduce: (1) the server workload, (2) the data transmission cost and (3) the query response time, for location-based services while providing an answer for a continuous region query. We compare our first strategy with a brute-force strategy and found that our strategy can significantly reduce the server workload and data transmission cost over the brute-force method. We compare our improved strategy with the original strategy and brute-force strategy. The experimental results show that the improved strategy achieves lower query response time than the original and brute-force strategy

Machine learning and privacy preserving algorithms for spatial and temporal sensing

Sensing physical and social environments are ubiquitous in modern mobile phones, IoT devices, and infrastructure-based settings. Information engraved in such data, especially the time and location attributes have unprecedented potential to characterize individual and crowd behaviour, natural and technological processes. However, it is challenging to extract abstract knowledge from the data due to its massive size, sequential structure, asynchronous operation, noisy characteristics, privacy concerns, and real time analysis requirements. Therefore, the primary goal of this thesis is to propose theoretically grounded and practically useful algorithms to learn from location and time stamps in sensor data. The proposed methods are inspired by tools from geometry, topology, and statistics. They leverage structures in the temporal and spatial data by probabilistically modeling noise, exploring topological structures embedded, and utilizing statistical structure to protect personal information and simultaneously learn aggregate information. Proposed algorithms are geared towards streaming and distributed operation for efficiency. The usefulness of the methods is argued using mathematical analysis and empirical experiments on real and artificial datasets

Efficient location privacy algorithm for Internet of Things (IoT) services and applications

© 2016 Elsevier Ltd. Location-based Services (LBS) have become a very important area for research with the rapid development of Internet of Things (IoT) technology and the ubiquitous use of smartphones and social networks in our daily lives. Although users can enjoy a lot of flexibility and conveniences from the LBS with IoT, they may also lose their privacy. Untrusted or malicious LBS servers with all users' information can track users in various ways or release personal data to third parties. In this work, we first analyze the current dummy-location selection (DLS) algorithm-an efficient location privacy preservation approach and design an attack algorithm for DLS (ADLS) for test emerging IoT security. For efficiently preserving user's location privacy, we propose a novel dummy location privacy-preserving (DLP) algorithm by considering both computational costs and various privacy requirements of different users. Extensive simulation experiments have been carried out to evaluate the efficiency of the proposed schemes. Evaluation results show that the ADLS algorithm has a high probability of identifying the user's real location out from chosen dummy locations in the DLS algorithm. Our proposed DLP algorithm has clear advantages over the DLS algorithm in term of lower probability of revealing the user's real location and improved computational cost and efficiency (i.e., time, speed, accuracy, and complexity) while preserve the same privacy level as DLS algorithm