Houtan Shirani-Mehr

Location privacy: going beyond K-anonymity, cloaking and anonymizers

With many location-based services, it is implicitly assumed that the location server receives actual users locations to respond to their spatial queries. Consequently, information customized to their locations, such as nearest points of interest can be provided. However, there is a major privacy concern over sharing such sensitive information with potentially malicious servers, jeopardizing users’ private information. The anonymity- and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches. We performed extensive experiments on both real-world and synthetic datasets to confirm the effectiveness of our approaches.

Efficient viewpoint assignment for urban texture documentation

We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. We have proposed to implement PTD in two steps [10]. At the first step, termed viewpoint selection, a minimum number of points in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we focus on the viewpoint assignment problem. We first prove that this problem is an NP-hard problem, and therefore, the optimal solution for viewpoint assignment fails to scale as the extent of the urban environment and the number of participating users grow. Subsequently, we propose a family of heuristics for efficient viewpoint assignment to reduce the assignment running time while ensuring an almost complete texture collection. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.

Blind evaluation of location based queries using space transformation to preserve location privacy

In this paper we propose a fundamental approach to perform the class of Range and Nearest Neighbor (NN) queries, the core class of spatial queries used in location-based services, without revealing any location information about the query in order to preserve users’ private location information. The idea behind our approach is to utilize the power of one-way transformations to map the space of all objects and queries to another space and resolve spatial queries blindly in the transformed space. Traditional encryption based techniques, solutions based on the theory of private information retrieval, or the recently proposed anonymity and cloaking based approaches cannot provide stringent privacy guarantees without incurring costly computation and/or communication overhead. In contrast, we propose efficient algorithms to evaluate KNN and range queries privately in the Hilbert transformed space. We also propose a dual curve query resolution technique which further reduces the costs of performing range and KNN queries using a single Hilbert curve. We experimentally evaluate the performance of our proposed range and KNN query processing techniques and verify the strong level of privacy achieved with acceptable computation and communication overhead.

Efficient Viewpoint Selection for Urban Texture Documentation

We envision participatory texture documentation (PTD) as a process in which a group of participants (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative/social collection of the urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. PTD is implemented in two steps. In the first step, minimum number of points in the urban environment are selected from which collection of maximum urban texture is possible. This step is called viewpoint selection . In the next step, the selected viewpoints are assigned to users (based on their preferences and constraints) for texture collection. This step is termed viewpoint assignment . In this paper, we focus on the viewpoint selection problem. We prove that this problem is NP-hard, and accordingly, propose a scalable (and efficient) heuristic with approximation guarantee for viewpoint selection. We study, profile and verify our proposed solution by extensive experiments.

A case study of participatory data transfer for urban temperature monitoring

The sensing systems that monitor physical environments rely on communication infrastructures (wired or wireless) to collect data from the sensors embedded in the environment. However, in many urban environments pre-existing communication infrastructures are not available, and installing new infrastructures is unjustifiably expensive and/or technically infeasible. For such environments, we envision Participatory Data Transfer (PDT) as an alternative communication medium that leverages users participation for data transfer. With PDT, users use mobile devices to receive data from sensors, and forward the sensed data through the ad hoc network of the mobile devices until the data is received by the data aggregators (i.e., data sinks). Sensor deployment and ad hoc routing/ networking are two related problems that are both extensively studied in the literature. However, to enable efficient deployment of PDT for sensing applications one needs to consider the requirements of the two aforementioned problems in conjunction. In this paper, we present a case study of PDT with which we explore the performance of PDTbased data transfer with a sample urban sensing application, namely, an urban temperature monitoring application. Our experimental case study is by simulation based on real datasets including GPS track data for more than 2000 vehicles in the city of Beijing. We discuss our observations based on this case study which can serve as directions to design application-specific optimal PDT mechanisms.

Users plan optimization for participatory urban texture documentation

We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high quality urban texture documentation. We propose to implement PTD in two steps. At the first step, termed viewpoint selection, a minimum number of viewpoints in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) with a desirable quality can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we define each of these steps and prove that both are NP-hard problems. Accordingly, we propose efficient algorithms to implement the viewpoint selection and assignment problems. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.