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- Creators: Xue, Guoliang
Description
The recent years have witnessed a rapid development of mobile devices and smart devices. As more and more people are getting involved in the online environment, privacy issues are becoming increasingly important. People’s privacy in the digital world is much easier to leak than in the real world, because every action people take online would leave a trail of information which could be recorded, collected and used by malicious attackers. Besides, service providers might collect users’ information and analyze them, which also leads to a privacy breach. Therefore, preserving people’s privacy is very important in the online environment.
In this dissertation, I study the problems of preserving people’s identity privacy and loca- tion privacy in the online environment. Specifically, I study four topics: identity privacy in online social networks (OSNs), identity privacy in anonymous message submission, lo- cation privacy in location based social networks (LBSNs), and location privacy in location based reminders. In the first topic, I propose a system which can hide users’ identity and data from untrusted storage site where the OSN provider puts users’ data. I also design a fine grained access control mechanism which prevents unauthorized users from accessing the data. Based on the secret sharing scheme, I construct a shuffle protocol that disconnects the relationship between members’ identities and their submitted messages in the topic of identity privacy in anonymous message submission. The message is encrypted on the mem- ber side and decrypted on the message collector side. The collector eventually gets all of the messages but does not know who submitted which message. In the third topic, I pro- pose a framework that hides users’ check-in information from the LBSN. Considering the limited computation resources on smart devices, I propose a delegatable pseudo random function to outsource computations to the much more powerful server while preserving privacy. I also implement efficient revocations. In the topic of location privacy in location based reminders, I propose a system to hide users’ reminder locations from an untrusted cloud server. I propose a cross based approach and an improved bar based approach, re- spectively, to represent a reminder area. The reminder location and reminder message are encrypted before uploading to the cloud server, which then can determine whether the dis- tance between the user’s current location and the reminder location is within the reminder distance without knowing anything about the user’s location information and the content of the reminder message.
In this dissertation, I study the problems of preserving people’s identity privacy and loca- tion privacy in the online environment. Specifically, I study four topics: identity privacy in online social networks (OSNs), identity privacy in anonymous message submission, lo- cation privacy in location based social networks (LBSNs), and location privacy in location based reminders. In the first topic, I propose a system which can hide users’ identity and data from untrusted storage site where the OSN provider puts users’ data. I also design a fine grained access control mechanism which prevents unauthorized users from accessing the data. Based on the secret sharing scheme, I construct a shuffle protocol that disconnects the relationship between members’ identities and their submitted messages in the topic of identity privacy in anonymous message submission. The message is encrypted on the mem- ber side and decrypted on the message collector side. The collector eventually gets all of the messages but does not know who submitted which message. In the third topic, I pro- pose a framework that hides users’ check-in information from the LBSN. Considering the limited computation resources on smart devices, I propose a delegatable pseudo random function to outsource computations to the much more powerful server while preserving privacy. I also implement efficient revocations. In the topic of location privacy in location based reminders, I propose a system to hide users’ reminder locations from an untrusted cloud server. I propose a cross based approach and an improved bar based approach, re- spectively, to represent a reminder area. The reminder location and reminder message are encrypted before uploading to the cloud server, which then can determine whether the dis- tance between the user’s current location and the reminder location is within the reminder distance without knowing anything about the user’s location information and the content of the reminder message.
ContributorsZhao, Xinxin (Author) / Xue, Guoliang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Huang, Dijiang (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
The presence of a rich set of embedded sensors on mobile devices has been fuelling various sensing applications regarding the activities of individuals and their surrounding environment, and these ubiquitous sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from concept to reality. MCS aims to outsource sensing data collection to mobile users and it could revolutionize the traditional ways of sensing data collection and processing. In the meantime, cloud computing provides cloud-backed infrastructures for mobile devices to provision their capabilities with network access. With enormous computational and storage resources along with sufficient bandwidth, it functions as the hub to handle the sensing service requests from sensing service consumers and coordinate sensing task assignment among eligible mobile users to reach a desired quality of sensing service. This paper studies the problem of sensing task assignment to mobile device owners with specific spatio-temporal traits to minimize the cost and maximize the utility in MCS while adhering to QoS constraints. Greedy approaches and hybrid solutions combined with bee algorithms are explored to address the problem.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
ContributorsWang, Zhijie (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2016