Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

Location Privacy in the Era of Big Data and Machine Learning

Location data of individuals is one of the most sensitive sources of information that once revealed to ill-intended individuals or service providers, can cause severe privacy concerns. In this thesis, we aim at preserving the privacy of users in telecommunication networks against untrusted service providers as well as improving their privacy in the publication of location datasets. For improving the location privacy of users in telecommunication networks, we consider the movement of users in trajectories and investigate the threats that the query history may pose on location privacy. We develop an attack model based on the Viterbi algorithm termed as Viterbi attack, which represents a realistic privacy threat in trajectories. Next, we propose a metric called transition entropy that helps to evaluate the performance of dummy generation algorithms, followed by developing a robust dummy generation algorithm that can defend users against the Viterbi attack. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. For privacy preserving data publishing, an enhanced framework for anonymization of spatio-temporal trajectory datasets termed the machine learning based anonymization (MLA) is proposed. The framework consists of a robust alignment technique and a machine learning approach for clustering datasets. The framework and all the proposed algorithms are applied to the Geolife dataset, which includes GPS logs of over 180 users in Beijing, China

Trajectory Privacy Preservation and Lightweight Blockchain Techniques for Mobility-Centric IoT

Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity on resource-constrained devices and can only provide conditional privacy when a set of authorities governs the blockchain. This dissertation addresses these challenges to develop efficient trajectory privacy-preservation and lightweight blockchain techniques for mobility-centric IoT. We develop a pruning-based technique by quantifying the relationship between trajectory privacy and delay for real-time geo-tagged queries. This technique yields higher trajectory privacy with a reduced delay than contemporary techniques while preventing a long-term observation attack. We extend our study with the consideration of the presence of non-geo-tagged data in a trajectory. We design an attack model to show the spatiotemporal correlation between the geo-tagged and non-geo-tagged data which undermines the privacy guarantee of existing techniques. In response, we propose a methodology that considers the spatial distribution of the data in trajectory privacy-preservation and improves existing solutions, in privacy and usability. With respect to blockchain, we design and implement one of the first blockchain storage management techniques utilizing the mobility of the devices. This technique reduces the required storage space of a blockchain and makes it lightweight for resource-constrained mobile devices. To address the trajectory privacy challenges in an authority-based blockchain under the short-range communication constraints of the devices, we introduce a silence-based one of the first technique to establish a balance between trajectory privacy and blockchain utility. The designed trajectory privacy- preservation techniques we established are light- weight and do not require an intermediary to guarantee trajectory privacy, thereby providing practical and efficient solution for different mobility-centric IoT, such as mobile crowdsensing and Internet of Vehicles

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy

A survey on security and privacy issues in IoV

As an up-and-coming branch of the internet of things, internet of vehicles (IoV) is imagined to fill in as a fundamental information detecting and processing platform for astute transportation frameworks. Today, vehicles are progressively being associated with the internet of things which empower them to give pervasive access to data to drivers and travelers while moving. Be that as it may, as the quantity of associated vehicles continues expanding, new prerequisites, (for example, consistent, secure, vigorous, versatile data trade among vehicles, people, and side of the road frameworks) of vehicular systems are developing. Right now, the unique idea of vehicular specially appointed systems is being changed into another idea called the internet of vehicles (IoV). We talk about the issues faced in implementing a secure IoV architecture. We examine the various challenges in implementing security and privacy in IoV by reviewing past papers along with pointing out research gaps and possible future work and putting forth our on inferences relating to each paper

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c