This research studies the problem of enabling users to verify that the results of analytical queries such as top k they receive from a potentially untrustworthy cloud are indeed correct. Existing work shows that it is possible for a data owner to create an authentication data structure (ADS) by which a cloud can build a verification object (VO) to prove the correctness of a query result. The current technique, however, has largely ignored the computation cost in VO construction and query result verification. In this paper, we extend and integrate Intersection tree (I-tree) and Merkle hash-tree (MH-tree) to develop a new ADS called Intersection Function Merkle Hash-tree (IFMH-tree). We propose two versions of the IFMH-tree, one-signature and multi-signature, and study their performance in supporting three representative types of analytic queries, including top-k, range, and KNN queries. Our results show that the new technique outperforms the existing solution to a large extent.

Data outsourcing is a cost-effective solution for data owners to tackle issues such as large volumes of data, huge number of users, and intensive computation needed for data analysis. They can simply upload their databases to a cloud and let it perform all management works, including query processing. One problem with this service model is how query issuers can verify the query results they receive are indeed correct. This concern is legitimate because, as a third party, clouds may not be fully trustworthy, and as a large data center, clouds are ideal targets for hackers. There has been significant work on query result verification, but most consider only simple queries where query results can be attained by checking the raw data against the query conditions directly. In this paper, we consider the problem of enabling users to verify the correctness of the results of analytic queries. Unlike simple queries, analytic queries involve ranking functions to score a database, which makes it difficult to build data structures for verification purposes. We propose two approaches, namelyone-signatureandmulti-signature, and show that they work well on three representative types of analytic queries, includingtop-k,range, andKNNqueries, through both analysis and experiments.

We introduce a new problem, namely, check-in time prediction where the goal is to predict the time when a given user will check-in to a location of interest. We design a novel Recurrent Spatio-Temporal Point Process (RSTPP) model for check-in time prediction. RSTPP addresses two key challenges: 1) Data scarcity due to uneven distribution of check-ins among users/locations. 2) User trajectories contain valuable information that is ignored by standard temporal point process which only considers historical event times. RSTPP is designed to learn the latent dependencies of event times over both historical events and spatio-temporal information about locations a user visited before check-in to the location of interest. We evaluate RSTPP on several real-world datasets, and it significantly outperforms state-of-the-art event time predicting techniques. Our work derives a set of practical implications that can benefit a wide spectrum of applications.

We introduce a new query primitive called Function Query (FQ). An FQ operates on a set of math functions and retrieves the functions whose output with a given input satisfies a query condition (e.g., being among top k, within a given range). While FQ finds its natural uses in querying a database of math functions, it can also be applied on a database of discrete values. We show that by interpreting the database as a set of user-defined functions, FQ can achieve the same functionality as existing analytic queries such as top-k query and scalar product query. We address the challenge of efficient execution of FQ. The core of our solution is a novel data structure called Intersection-tree. Our research takes advantage of the fact that 1) the intersections of a set of continuous functions partition their domain into a number of subdomains, and 2) in each of these subdomains, the functions can be sorted based on their output. We evaluate the performance of the proposed techniques through analysis, prototyping, and experiments using both synthetic and real-world data. When querying a database of functions, our techniques scale well. When applied on a database of discrete values, our techniques are more versatile and outperform existing techniques in terms of various performance metrics.

An effective way to visualize the prediction of deep neural networks on an image is to decompose the prediction into the contribution of units (pixels or patches). In the existing works, these units are largely considered independently, thus limiting the performance of visualization. In this paper, we propose a new predication visualization method that uses super-pixel as a contribution unit. Moreover, our method takes into consideration of the interaction of adjacent super-pixels. We implement our technique and evaluate its performance with various images. Our results show its excellent performance.

Consider a database where each record represents a math function. A third party is in charge of processing queries over this database and we want to provide a mechanism for users to verify the correctness of their query results. Here each query, referred to as a Function Query (FQ), retrieves the functions whose computation results with user-supplied arguments satisfy certain conditions (e.g., within a certain range). We present authentication solutions that work on a variety of functions, including univariate linear function, multivariate linear function, and multivariate high degree function. Our solutions are based on the fact that the functions can be sorted in the subdomains defined by their intersections and thus can be chained to produce a signature mesh for query result verification. We study the performance of the proposed techniques through theoretical analysis, simulation and empirical study, and include the results in this paper.

In mobile object database systems, both query issuers and queried objects are subject to location privacy intrusion. One solution to this problem is to have users reduce their location resolution when making location update. Such location cloaking allows mobile objects to achieve a desired level of protection, but may not produce accurate query results. Alternatively, one can apply cryptography techniques such as secure multiparty computation to compute the spatial relationship among mobile objects without having mobile objects to disclose their location at all. This strategy produces high quality query results, but in general are computation-intensive, especially when a large number of mobile objects are involved. In this paper, we present a hybrid approach that mitigates the above dilemma. Our idea is to compute approximate query results based on cloaked location information and then refine query results by applying homomorphic encryption. We demonstrate that this approach can be used for efficient and privacy-preserving processing of KNN queries and evaluate its performance through simulation.

A spatial monitoring query (SMQ) retrieves the set of mobile nodes that satisfy some spatial constraints, and provides real-time updates whenever this set of nodes changes. Efficient processing of such queries is essential to moving objects database management. Existing techniques rely on one or more central servers for query management and assume each mobile node can communicate with some server directly. These limitations prevent them from being used in application scenarios where no such server exists. This paper assumes a mobile peer-to-peer system where mobile nodes are the only computing devices, and investigates the challenges of allowing mobile nodes to collaborate in query processing. We present a cost-effective technique to process a primitive type of SMQs and then show that other types of queries can be converted into the primitive type of queries. As such, different types of queries can now be supported within a common platform and without relying on any stationary server. We also evaluate, through both mathematical analysis and simulation, the performance of the proposed platform in terms of mobile communication costs incurred in query processing.

Most existing cloaking techniques only focus on achieving location k-anonymity. However, maintaining location k-anonymity alone is not enough to counter query homogeneity attacks. In this paper, we first define the query l-diversity concept in location-based services, and then propose a technique to achieve query l-diversity. The proposed technique is compared with the improved Interval Cloak technique using simulation; and the extensive results indicate that our technique is better in protecting user privacy.

A continuous k nearest neighbor (cKNN) query is a query that continuously returns a set of k nearest moving objects (mobile hosts) to a given query point. For example, report three nearest moving sensors to a given location continuously. Most existing research efforts focus on centralized solutions. In a mobile peer-to-peer network (M-P2P), a centralized approach incurs expensive communication cost. In this paper, we propose Safe-Time - a distributed solution for cKNN given a stationary query point for M-P2P. The two key features are as follows. 1) Actual execution of a cKNN query is not needed during a safe-time period since the query result is guaranteed to remain the same during this period. 2) Once the safe-time expires, execution of a cKNN query involves only objects in a circular band of width equal to the estimated distance between the kthand the k + 1thnearest neighbors. To further reduce communication cost for dense queries, we introduce Unite-Safe-Time that executes one virtual query derived from nearby queries instead of executing each of them separately. Our simulation result shows that the proposed distributed solutions outperform a centralized solution under a range of conditions. Safe-Time incurs up to 2/3 less communication cost compared to a centralized solution. Unite-Safe-Time shows up to 1/3 less communication cost than Safe-Time in our study.

A major concern for large-scale deployment of location-based services (LBSs) is the potential abuse of their client location data, which may imply sensitive personal information. Location privacy protection is challenging because a location itself may reveal a subject's identity. To support location anonymity, existing research reduces location resolution by ensuring each location reported to a service provider is a cloaking area that contains at least K mobile nodes. This strategy is effective when each location update can be considered as an independent event. In this paper, we investigate location anonymity in the context of continuous LBSs, which require frequent location updates from service users. Knowing that a user is inside a cloaking area constrains its position in the next cloaking area. Thus, simply ensuring each cloaking area contains at least K users does not give a user K-anonymity protection. We propose to measure the anonymity degree of a cloaking area using entropy, which takes into account not only the number of the entities inside, but also their anonymity probability distribution. To find a cloaking area that can provide a given level of anonymity protection and is also as small as possible, we present a novel technique with a polynomial time complexity. The effectiveness of our techniques is studied under various conditions using location data synthetically generated using real road maps and traffic volume data. The results show that our techniques can indeed protect user anonymity at a desired level, and at the same time, minimize the size of each cloaking area, allowing users to receive high quality services.

This paper investigates a new problem of sharing location dependent experiences among mobile hosts in mobile adhoc networks. An experience is location and observer dependent. In other words, experiences of different people witnessing the same event may be quite different. The ability to retrieve prior experiences observed in a given area in advance is very useful for newcomers wishing to enter the same vicinity. Solving this problem is vital for important applications such as hurricane rescue missions, combat missions, and deep space or deep sea exploration. In this paper, we propose a distributed solution that lets mobile hosts share their experiences efficiently. Our simulation results show that our approach significantly outperforms a centralized approach.

We propose a framework, called overlay subscription network (OSN), for live Internet TV broadcast, where a subscriber can choose to watch at any time. This framework allows the source server to incrementally build a topology graph that contains the network connections not only from the server to each subscriber, but also among the subscribers themselves. With such a topology graph in place, we consider efficient overlay multicast for scalable OSN services. We first show that idling nodes, which do not receive video data for their own playback, can actually be used for data forwarding to significantly reduce the cost of overlay multicast. In light of this observation, we then propose a novel overlay multicast technique that distinguishes itself from existing schemes with these three aspects. First, the proposed technique is centered on the topology graph and can take advantage of the actual network connections among the subscribing nodes. Second, the new scheme is able to find and incorporate appropriate idling nodes in multicast to reduce network traffic. Third, with our approach, a node can be used in multiple multicast trees for data forwarding to improve the overall system performance. We evaluate the performance of the proposed technique through simulation. Our extensive studies show that the proposed framework has the potential to enable the Internet, a vehicle up to date mainly for transferring text and image data, for large-scale and cost-effective TV broadcast

We consider in this paper how to leverage heterogeneous mobile computing capability for efficient processing of real-time range-monitoring queries. In our environment, each mobile object is associated with a resident domain and when an object moves, it monitors its spatial relationship with its resident domain and the monitoring areas inside it. An object reports its location to server whenever its movement affects any query results (i.e., crossing any query boundaries) or it moves out of its resident domain. In the first case, the server updates the affected query results accordingly while in the second case, the server determines a new resident domain for the object. This distributive approach is able to provide accurate query results and real-time monitoring updates with minimal location update and server processing costs. In addition, the new scheme allows a mobile object to negotiate a resident domain based on its computing capability. Thus, a more capable object can have a larger resident domain reducing its chance of having to request a new resident domain because of moving out of it. This feature makes the new approach highly adaptive to the heterogeneity of mobile objects. In our performance study, we compare it with an existing approach using simulation. The study shows that the new technique is many times better in reducing mobile communication and server processing costs.