Tanzima Hashem

Group trip planning queries in spatial databases

In this paper we discuss a new type of query in Spatial Databases, called the Trip Planning Query (TPQ). Given a set of points of interest P in space, where each point belongs to a specific category, a starting point S and a destination E, TPQ retrieves the best trip that starts at S, passes through at least one point from each category, and ends at E. For example, a driver traveling from Boston to Providence might want to stop to a gas station, a bank and a post office on his way, and the goal is to provide him with the best possible route (in terms of distance, traffic, road conditions, etc.). The difficulty of this query lies in the existence of multiple choices per category. In this paper, we study fast approximation algorithms for TPQ in a metric space. We provide a number of approximation algorithms with approximation ratios that depend on either the number of categories, the maximum number of points per category or both. Therefore, for different instances of the problem, we can choose the algorithm with the best approximation ratio, since they all run in polynomial time. Furthermore, we use some of the proposed algorithms to derive efficient heuristics for large datasets stored in external memory. Finally, we give an experimental evaluation of the proposed algorithms using both synthetic and real datasets.

Safeguarding location privacy in wireless ad-hoc networks

We present a novel algorithm that safeguards the location privacy of users accessing location-based services via mobile devices. Our technique exploits the capability of mobile devices to form wireless ad-hoc networks in order to hide a users identity and position. Local ad-hoc networks enable us to separate an agents request for location information, the query initiator, from the agent that actually requests this service on its behalf, the query requestor. Since a query initiator can select itself or one of the k - 1 agents in its ad-hoc network as a query requestor, the query initiator remains k-anonymous. In addition, the location revealed to the location service provider is a rectangle instead of an exact coordinate. We develop an anonymous selection algorithm that selects a query requestor with near-uniform randomness, which is a key component to ensure anonymity in an ad-hoc network. Our experiments show that a system can ensure a high quality of service and maintain a high degree of privacy in terms of anonymity and obfuscation while accessing location-based services.

Don't trust anyone: Privacy protection for location-based services

We present a decentralized approach that exploits the capability of mobile devices to form wireless personal ad-hoc networks in order to protect the privacy of users who access location-based services. The novelty of our approach is that users do not need to trust any party such as an intermediary server or peers with their locations and identities. We propose efficient algorithms for users to compute a k-anonymous imprecise location and to randomly select one of her peers with uniform probability who forwards the service request on behalf of the user. Our experimental evaluation shows that using our approach a user can enjoy a high quality of service with a high degree of privacy.

Countering overlapping rectangle privacy attack for moving kNN queries

An important class of LBSs is supported by the moving k nearest neighbor (MkNN) query, which continuously returns the k nearest data objects for a moving user. For example, a tourist may want to observe the five nearest restaurants continuously while exploring a city so that she can drop in to one of them anytime. Using this kind of services requires the user to disclose her location continuously and therefore may cause privacy leaks derived from the users locations. A common approach to protecting a users location privacy is the use of imprecise locations (e.g., regions) instead of exact positions when requesting LBSs. However, simply updating a users imprecise location to a location-based service provider (LSP) cannot ensure a users privacy for an MkNN query: continuous disclosure of regions enable LSPs to refine more precise location of the user. We formulate this type of attack to a users location privacy that arises from overlapping consecutive regions, and provide the first solution to counter this attack. Specifically, we develop algorithms which can process an MkNN query while protecting the users privacy from the above attack. Extensive experiments validate the effectiveness of our privacy protection technique and the efficiency of our algorithm.

Efficient Computation of Trips with Friends and Families

A group of friends located at their working places may want to plan a trip to visit a shopping center, have dinner at a restaurant, watch a movie at a theater, and then finally return to their homes with the minimum total trip distance. For a group of spatially dispersed users a group trip planning (GTP) query returns points of interests (POIs) of different types such as a shopping center, a restaurant and a movie theater that minimize the aggregate trip distance for the group. The aggregate trip distance could be the sum or maximum of the trip distances of all users in the group, where the users travel from their source locations via the jointly visited POIs to their individual destinations. In this paper, we develop both optimal and approximation algorithms for GTP queries for both Euclidean space and road networks. Processing GTP queries in real time is a computational challenge as trips involve POIs of multiple types and computation of aggregate trip distances. We develop novel techniques to refine the POI search space for a GTP query based on geometric properties of ellipses, which in turn significantly reduces the number of aggregate trip distance computations. An extensive set of experiments on a real and synthetic datasets shows that our approach outperforms the most competitive approach on an average by three orders of magnitude in terms of processing time.

Protecting privacy for group nearest neighbor queries with crowdsourced data and computing

User privacy in location-based services (LBSs) has become an important research area. We introduce a new direction to protect user privacy that evaluates LBSs with crowdsourced data and computation and eliminates the role of a location-based service provider. We focus on the group nearest neighbor (GNN) query that allows a group to meet at their nearest point of interest such as a restaurant that minimizes the total or maximum distance of the group. We develop a crowdsource-based approach, called PrivateMeetUp, to evaluate GNN queries in a privacy preserving manner and implement a working prototype of PrivateMeetUp.

Efficient Computation of Group Optimal Sequenced Routes in Road Networks

The proliferation of location-based social networks allows people to access location-based services as a group. We address Group Optimal Sequenced Route (GOSR) queries that enable a group to plan a trip with a minimum aggregate trip distance. The trip starts from the source locations of the group members, goes via a predefined sequence of different point of interests (POIs) such as a restaurant, shopping center and movie theater, and ends at the destination locations of the group members. The aggregate trip distance can be the total or the maximum trip distance of the group members. We introduce a novel approach to efficiently compute group optimal sequenced routes in road networks. We exploit elliptical properties to refine the POI search space and develop efficient algorithms for GOSR queries. Experiments show that our approach outperforms a naive approach significantly in terms of processing time and I/Os.

User Interaction Based Community Detection in Online Social Networks

Discovering meaningful communities based on the interactions of different people in online social networks (OSNs) is an active research topic in recent years. However, existing interaction based community detection techniques either rely on the content analysis or only consider underlying structure of the social network graph, while identifying communities in OSNs. As a result, these approaches fail to identify active communities, i.e., communities based on actual interactions rather than mere friendship. To alleviate the limitations of existing approaches, we propose a novel solution of community detection in OSNs. The key idea of our approach comes from the following observations: (i) the degree of interaction between each pair of users can widely vary, which we term as the strength of ties, and (ii) for each pair of users, the interactions with mutual friends, which we term the group behavior, play an important role to determine their belongingness to the same community. Based on these two observations, we propose an efficient solution to detect communities in OSNs. The detailed experimental study shows that our proposed algorithm significantly outperforms state-of-the-art techniques for both real and synthetic datasets

SafeStreet: empowering women against street harassment using a privacy-aware location based application

Sexual harassment of women in public places (e.g., foot-paths, buses, and shopping malls) of major cities in developing countries is a growing concern. These harassments can happen in various forms ranging from commenting, catcalling, and staring to touching and groping, to attacking and raping. Though, the most severe form of harassments such as attacking and raping get some attention from the society, NGOs and law-enforcement agencies, unfortunately, other forms of harassments that are more widespread in public places remain largely un-attended or ignored in our conservative society. However, these harassments are more common and can have various negative psychological impacts on women that include a persistent feeling of insecurity, loss of self-esteem, restricted participation in daily life activities in public places. In this paper, we propose a crowd-powered privacy-aware location based mobile application, SafeStreet, that empowers women in public places against sexual harassments. SafeStreet allows a women to privately capture and share her own experiences in the street. SafeStreet enables a women to find a safe path, i.e., the path to a destination that has less harassment hazard, at any point of time.

Trip planning queries with location privacy in spatial databases

Privacy has become a major concern for the users of location-based services (LBSs) and researchers have focused on protecting user privacy for different location-based queries. In this paper, we propose techniques to protect location privacy of users for trip planning (TP) queries, a novel type of query in spatial databases. A TP query enables a user to plan a trip with the minimum travel distance, where the trip starts from a source location, goes through a sequence of points of interest (POIs) (e.g., restaurant, shopping center), and ends at a destination location. Due to privacy concerns, users may not wish to disclose their exact locations to the location-based service provider (LSP). In this paper, we present the first comprehensive solution for processing TP queries without disclosing a user’s actual source and destination locations to the LSP. Our system protects the user’s privacy by sending either a false location or a cloaked location of the user to the LSP but provides exact results of the TP queries. We develop a novel technique to refine the search space as an elliptical region using geometric properties, which is the key idea behind the efficiency of our algorithms. To further reduce the processing overhead while computing a trip from a large POI database, we present an approximation algorithm for privacy preserving TP queries. Extensive experiments show that the proposed algorithms evaluate TP queries in real time with the desired level of location privacy.