ANDROID PROJECTS - ABSTRACTS
A Scalable Server Architecture for Mobile Presence Services in Social Network Applications
Social network applications are becoming increasingly popular on mobile devices. A mobile presence service is an essential component of a social network application because it maintains each mobile user's presence information, such as the current status (online/offline), GPS location and network address, and also updates the user's online friends with the information continually.
If presence updates occur frequently, the enormous number of messages distributed by presence servers may lead to a scalability problem in a large-scale mobile presence service. To address the problem, we propose an efficient and scalable server architecture, called Presence Cloud, which enables mobile presence services to support large-scale social network applications.
When a mobile user joins a network, Presence Cloud searches for the presence of his/her friends and notifies them of his/her arrival. Presence Cloud organizes presence servers into a quorum-based server-to-server architecture for efficient presence searching. It also leverages a directed search algorithm and a one-hop caching strategy to achieve small constant search latency.
We analyze the performance of Presence Cloud in terms of the search cost and search satisfaction level. The search cost is defined as the total number of messages generated by the presence server when a user arrives; and search satisfaction level is defined as the time it takes to search for the arriving user's friend list. The results of simulations demonstrate that Presence Cloud achieves performance gains in the search cost without compromising search satisfaction.
A Fast Clustering- Based Feature Subset Selection Algorithm for High- Dimensional Data
Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view.
While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm (FAST) is proposed and experimentally evaluated in this paper.
The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent, the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features.
To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Extensive experiments are carried out to compare FAST and several representative feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with respect to four types of well-known classifiers, namely, the probability based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection.
The results, on 35 publicly available real-world high-dimensional image, microarray, and text data, demonstrate that the FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers
A Generalized Flow-based Method for Analysis of Implicit Relationships on Wikipedia
ABSTRACT
We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relationships between two objects exist: in Wikipedia, an explicit relationship is represented by a single link between the two pages for the objects, and an implicit relationship is represented by a link structure containing the two pages.
Some of the previously proposed methods for measuring relationships are cohesion-based methods, which underestimate objects having high degrees, although such objects could be important in constituting relationships in Wikipedia.
The other methods are inadequate for measuring implicit relationships because they use only one or two of the following three important factors: distance, connectivity, and cocitation. We propose a new method using a generalized maximum flow which reflects all the three factors and does not underestimate objects having high degree.
We confirm through experiments that our method can measure the strength of a relationship more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relationship. We explain that mining elucidatory objects would open a novel way to deeply understand a
A Proxy-Based Approach to Continuous Location-Based Spatial Queries in Mobile Environments
Abstract: Caching valid regions of spatial queries at mobile clients is effective in reducing the number of queries submitted by mobile clients and query load on the server. However, mobile clients suffer from longer waiting time for the server to compute valid regions. We propose in this paper a proxy-based approach to continuous nearest-neighbor (NN) and window queries.
The proxy creates estimated valid regions (EVRs) for mobile clients by exploiting spatial and temporal locality of spatial queries. For NN queries, we devise two new algorithms to accelerate EVR growth, leading the proxy to build effective EVRs even when the cache size is small. On the other hand, we propose to represent the EVRs of window queries in the form of vectors, called estimated window vectors (EWVs), to achieve larger estimated valid regions.
This novel representation and the associated creation algorithm result in more effective EVRs of window queries. In addition, due to the distinct characteristics, we use separate index structures, namely EVR-tree and grid index, for NN queries and window queries, respectively.
To further increase efficiency, we develop algorithms to exploit the results of NN queries to aid grid index growth, benefiting EWV creation of window queries. Similarly, the grid index is utilized to support NN query answering and EVR updating. We conduct several experiments for performance evaluation. The experimental results show that the proposed approach significantly outperforms the existing proxy-based approaches.
AML Efficient Approximate Membership Localization within a Web-based Join Framework
ABSTRACT
In this paper, we propose a new type of Dictionary-based Entity Recognition Problem, named Approximate Membership Localization (AML). The popular Approximate Membership Extraction (AME) provides a full coverage to the true matched substrings from a given document, but many redundancies cause a low efficiency of the AME process and deteriorate the performance of real-world applications using the extracted substrings.
The AML problem targets at locating non overlapped substrings which is a better approximation to the true matched substrings without generating overlapped redundancies. In order to perform AML efficiently, we propose the optimized algorithm P-Prune that prunes a large part of overlapped redundant matched substrings before generating them.
Our study using several real-word data sets demonstrates the efficiency of P-Prune over a baseline method. We also study the AML in application to a proposed web-based join framework scenario which is a search-based approach joining two tables using dictionary-based entity recognition from web documents. The results not only prove the advantage of AML over AME, but also demonstrate the effectiveness of our search-based approach
Analysis of Distance-Based Location Management in Wireless Communication Networks
ABSTRACT
The performance of dynamic distance-based location management schemes (DBLMS) in wireless communication networks is analyzed. A Markov chain is developed as a mobility model to describe the movement of a mobile terminal in 2D cellular structures. The paging area residence time is characterized for arbitrary cell residence time by using the Markov chain.
The expected number of paging area boundary crossings and the cost of the distance-based location update method are analyzed by using the classical renewal theory for two different call handling models. For the call plus location update model, two cases are considered.
In the first case, the inter call time has an arbitrary distribution and the cell residence time has an exponential distribution. In the second case, the inter call time has a hyper-Erlang distribution and the cell residence time has an arbitrary distribution.
For the call without location update model, both inter call time and cell residence time can have arbitrary distributions. Our analysis makes it possible to find the optimal distance threshold that minimizes the total cost of location management in a DBLMS
Anonymization of Centralized and Distributed Social Networks by Sequential Clustering
We study the problem of privacy-preservation in social networks. We consider the distributed setting in which the network data is split between several data holders. The goal is to arrive at an anonymized view of the unified network without revealing to any of the data holders information about links between nodes that are controlled by other data holders.
To that end, we start with the centralized setting and offer two variants of an anonymization algorithm which is based on sequential clustering (Sq). Our algorithms significantly outperform the SaNGreeA algorithm due to Campan and Truta which is the leading algorithm for achieving anonymity in networks by means of clustering.
We then devise secure distributed versions of our algorithms. To the best of our knowledge, this is the first study of privacy preservation in distributed social networks. We conclude by outlining future research proposals in that direction.
Cloud FTP: A Case Study of Migrating Traditional Applications to the Cloud
ABSTRACT:
The cloud computing is growing rapidly for it offers on-demand computing power and capacity. The power of cloud enables dynamic scalability of applications facing various business requirements. However, challenges arise when considering the large amount of existing applications.
In this work we propose to move the traditional FTP service to the cloud. We implement FTP service on Windows Azure Platform along with the auto-scaling cloud feature. Based on this, we implement a benchmark to measure the performance of our Cloud FTP.
This case study illustrates the potential benefits and technical issues associated with the migration of the traditional applications to the clouds.
Crowd sourced Trace Similarity with Smartphones
Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS, accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced computing services, which contribute to the solution of complex problems in a distributed manner.
In this work, we leverage such a computing paradigm to solve efficiently the following problem: comparing a query trace $(Q)$ against a crowd of traces generated and stored on distributed smartphones.
Our proposed framework, coined $({\rm SmartTrace}^+)$, provides an effective solution without disclosing any part of the crowd traces to the query processor. $({\rm SmartTrace}^+)$, relies on an in-situ data storage model and intelligent top-K query processing algorithms that exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to $(Q)$. We evaluate our algorithms on both synthetic and real workloads.
We describe our prototype system developed on the Android OS. The solution is deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that computations over $({\rm SmartTrace}^+)$ result in substantial energy conservation; in addition, results can be computed faster than competitive approaches
Discovery and Verification of Neighbor Positions in Mobile Ad Hoc Networks
A growing number of ad hoc networking protocols and location-aware services require that mobile nodes learn the position of their neighbors. However, such a process can be easily abused or disrupted by adversarial nodes. In absence of a priori trusted nodes, the discovery and verification of neighbor positions presents challenges that have been scarcely investigated in the literature.
In this paper, we address this open issue by proposing a fully distributed cooperative solution that is robust against independent and colluding adversaries, and can be impaired only by an overwhelming presence of adversaries.
Results show that our protocol can thwart more than 99 percent of the attacks under the best possible conditions for the adversaries, with minimal false positive rates.
Distributed Web Systems Performance Forecasting using Turning Bands Method
ABSTRACT:
Development of distributed computer systems (DCSs) in networked industrial and manufacturing applications on the World Wide Web (WWW) platform, including service-oriented architecture and Web of Things QoS-aware systems, it has become important to predict theWeb performance.
In this paper, we presentWeb performance prediction in time and in space by making a forecast of a Web resource downloading using the Turning Bands (TB) geostatistical simulation method.
Real-life data for the research were obtained in an active experiment conducted by our multi- agent measurement system MWING performing monitoring of a group of Web servers worldwide from agents localized in different geographical localizations in Poland.
The results show good quality of Web performance prediction made by means of the TB method, especially in the case when European Web servers were monitored by an MWING agent localized in Gliwice, Poland
Dynamic Personalized Recommendation on Sparse Data
Abstract: Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance.
Nowadays the internet has become an indispensable part of our lives, and it provides a platform for enterprises to deliver information about products and services to the customers conveniently. As the amount of this kind of information is increasing rapidly, one great challenge is ensuring that proper content can be delivered quickly to the appropriate customers. Personalized recommendation is a desirable way to improve customer satisfaction and retention.
There are mainly three approaches to recommendation engines based on different data analysis methods, i.e., rule-based, content-based and collaborative filtering. Among them, collaborative filtering (CF) requires only data about past user behavior like ratings, and its two main approaches are the neighborhood methods and latent factor models.
The neighborhood methods can be user-oriented or item-oriented. They try to find like-minded users or similar items on the basis of co-ratings, and predict based on ratings of the nearest neighbors. Latent factor models try to learn latent factors from the pattern of ratings using techniques like matrix mfactorization and use the factors to compute the usefulness of items to users. CF has made great success and been proved to perform well in scenarios where user preferences are relatively stat
Evaluating Data Reliability an Evidential Answer with Application to a Web-Enabled Data Warehouse
ABSTRACT:
There are many available methods to integrate information source reliability in an uncertainty representation, but there are only a few works focusing on the problem of evaluating this reliability.
However, data reliability and confidence are essential components of a data warehousing system, as they influence subsequent retrieval and analysis. In this paper, we propose a generic method to assess data reliability from a set of criteria using the theory of belief functions. Customizable criteria and insightful decisions are provided.
The chosen illustrative example comes from real-world data issued from the Sym’Previus predictive microbiology oriented data warehouse
Exploiting Ubiquitous Data Collection for Mobile users in Wireless Sensor Networks
ABSTRACT:
We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data.
The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance.
Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement.
We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.
Finding Rare Classes Active Learning with Generative and Discriminative Models
ABSTRACT
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields but fully supervised learning of a rare class classifier is prohibitively costly in labeling effort. There has therefore been increasing interest both in active discovery: to identify new classes quickly, and active learning: to train classifier with minimal supervision.
These goals occur together in practice and are intrinsically related because examples of each class are required to train a classifier. Nevertheless, very few studies have tried to optimize them together, meaning that data mining for rare classes in new domains makes inefficient use of human supervision.
Developing active learning algorithms to optimize both rare class discovery and classification simultaneously is challenging because discovery and classification have conflicting requirements in query criteria.
In this paper we address these issues with two contributions: a unified active learning model to jointly discover new categories and learn to classify them by adapting query criteria online; and a classifier combination algorithm that switches generative and discriminative classifiers as learning progresses. Extensive evaluation on a batch of standard UCI and vision datasets demonstrates the superiority of this approach over existing methods
Ranking on Data Manifold with Sink Points
ABSTRACT:
Ranking is an important problem in various applications, such as Information Retrieval (IR), natural language processing, computational biology, and social sciences. Many ranking approaches have been proposed to rank objects according to their degrees of relevance or importance. Beyond these two goals, diversity has also been recognized as a crucial criterion in ranking.
Top ranked results are expected to convey as little redundant information as possible, and cover as many aspects as possible. However, existing ranking approaches either take no account of diversity, or handle it separately with some heuristics. In this paper, we introduce a novel approach, Manifold Ranking with Sink Points (MRSPs), to address diversity as well as relevance and importance in ranking.
Specifically, our approach uses a manifold ranking process over the data manifold, which can naturally find the most relevant and important data objects. Meanwhile, by turning ranked objects into sink points on data manifold, we can effectively prevent redundant objects from receiving a high rank. MRSP not only shows a nice convergence property, but also has an interesting and satisfying optimization explanation.
We applied MRSP on two application tasks, update summarization and query recommendation, where diversity is of great concern in ranking. Experimental results on both tasks present a strong empirical performance of MRSP as compared to existing ranking approaches
Region-based Foldings in Process Discovery
ABSTRACT:
A central problem in the area of Process Mining is to obtain a formal model that represents the processes that are conducted in a system. If realized, this simple motivation allows for powerful techniques that can be used to formally analyze and optimize a system, without the need to resort to its semiformal and sometimes inaccurate specification.
The problem addressed in this paper is known as Process Discovery: to obtain a formal model from a set of system executions. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets) from a set of traces. On its genuine form, the theory is applied on an automaton and therefore one should convert the traces into an acyclic automaton in order to apply these techniques.
Given that the complexity of the region-based techniques depends on the size of the input automata, revealing the underlying cycles and folding the initial automaton can incur in a significant complexity alleviation of the region-based techniques. In this paper, we follow this idea by incorporating region information in the cycle detection algorithm, enabling the identification of complex cycles that cannot be obtained efficiently with state-of-the-art techniques.
The experimental results obtained by the devised tool suggest that the techniques presented in this paper are a big step into widening the application of the theory of regions in Process Mining for industrial scenarios.
Research in Progress - Defending Android Smartphones from Malware Attacks
Smart phones are becoming enriched with confidential information due to their powerful computational capabilities and attractive communications features. The Android smart phone is one of the most widely used platforms by businesses and users alike. This is partially because Android smart phones use the free, open-source Linux as the underlying operating system, which allows development of applications by any software developer.
This research study aims to explore security risks associated with the use of Android smart phones and the sensitive information they contain, the researcher devised a survey questionnaire to investigate and further understand security threats targeting Android smart phones.
The survey also intended to study the scope of malware attacks targeting Android phones and the effectiveness of existing defense measures. The study surveyed the average Android users as the target population to understand how they perceive security and what security controls they use to protect their smart phones
Scalable and Secure Sharing of Personal Health Records in Cloud Computing using Attribute based Encryption
Abstract:
Personal health record (PHR) is an emerging patient-centric model of health information exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties.
To assure the patients’ control over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible access and efficient user evocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control.
In this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semi-trusted servers. To achieve fine-grained and scalable data access control for PHRs, we leverage attribute based encryption (ABE) techniques to encrypt each patient’s PHR file. Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users.
A high degree of patient privacy is guaranteed simultaneously by exploiting multi-authority ABE. Our cheme also enables dynamic modification of access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented which show the security, scalability and efficiency of our proposed scheme
Secure Encounter-based Mobile Social Networks Requirements Designs and Tradeoffs
Abstract: Encounter-based social networks link users who share a location at the same time, as opposed to traditional social network paradigms of linking users who have an offline friendship. This approach presents fundamentally different challenges from those tackled by previous designs. In this paper, we explore functional and security requirements for these new systems, such as availability, security, and privacy, and present several design options for building secure encounter-based social networks.
We examine one recently proposed encounter-based social network design and compare it to a set of idealized security and functionality requirements. We show that it is vulnerable to several attacks, including impersonation, collusion, and privacy breaching, even though it was designed specifically for security.
Mindful of the possible pitfalls, we construct a flexible framework for secure encounter-based social networks, which can be used to construct networks that offer different security, privacy, and availability guarantees. We describe two example constructions derived from this framework, and consider each in terms of the ideal requirements.
Some of our new designs fulfill more requirements in terms of system security, reliability, and privacy than previous work. We also evaluate real-world performance of one of our designs by implementing a proof-of-concept iPhone application called MeetUp. Experiments highlight the potential of our system.
Security Analysis of a Single Sign-On Mechanism for Distributed Computer Networks
ABSTRACT:
In this paper, however, we demonstrative that their scheme is actually insecure as it fails to meet credential privacy and soundness of authentication. Specifically, we present two impersonation attacks.
The first attack allows a malicious service provider, who has successfully communicated with a legal user twice, to recover the user’s credential and then to impersonate the user to access resources and services offered by other service providers. In another attack, an outsider without any credential may be able to enjoy network services freely by impersonating any legal user or a nonexistent user.
We identify the flaws in their security arguments to explain why attacks are possible against their SSO scheme. Our attacks also apply to another SSO scheme proposed by Hsu and Chuang, which inspired the design of the Chang–Lee scheme. Moreover, by employing an efficient verifiable encryption of RSA signatures proposed by Ateniese, we propose an improvement for repairing the Chang–Lee scheme.
SPOC: A Secure and Privacy-Preserving Opportunistic Computing Framework for Mobile-Healthcare Emergency
With the pervasiveness of smart phones and the advance of wireless body sensor networks (BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a pervasive environment for better health monitoring, has attracted considerable interest recently.
However, the flourish of m-Healthcare still faces many challenges including information security and privacy preservation. In this paper, we propose a secure and privacy-preserving opportunistic computing framework, called SPOC, for m-Healthcare emergency. With SPOC, smart phone resources including computing power and energy can be opportunistically gathered to process the computing-intensive personal health information (PHI) during m-Healthcare emergency with minimal privacy disclosure.
In specific, to leverage the PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare emergency, we introduce an efficient user-centric privacy access control in SPOC framework, which is based on an attribute-based access control and a new privacy-preserving scalar product computation (PPSPC) technique, and allows a medical user to decide who can participate in the opportunistic computing to assist in processing his overwhelming PHI data.
Detailed security analysis shows that the proposed SPOC framework can efficiently achieve user-centric privacy access control in m-Healthcare emergency. In addition, performance evaluations via extensive simulations demonstrate the SPOC's effectiveness in term of providing high-reliable-PHI process and transmission while minimizing the privacy disclosure during m-Healthcare emergency.
SSD A Robust RF Location Fingerprint Addressing Mobile Devices’ Heterogeneity
ABSTRACT: Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions.
We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both Access- Point (AP)-based localization and Mobile-Node (MN)-assisted localization.
We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device.
We also compare these SSD-based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD-based algorithms have better accuracy
Target Tracking and Mobile Sensor Navigation in Wireless Sensor Networks
ABSTRACT: This work studies the problem of tracking signal-emitting mobile targets using navigated mobile sensors based on signal reception. Since the mobile target’s maneuver is unknown, the mobile sensor controller utilizes the measurement collected by a wireless sensor network in terms of the mobile target signal’s time of arrival (TOA).
The mobile sensor controller acquires the TOA measurement information from both the mobile target and the mobile sensor for estimating their locations before directing the mobile sensor’s movement to follow the target. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semidefinite programming (SDP) relaxation, and apply a cubic function for mobile sensor navigation.
We estimate the location of the mobile sensor and target jointly to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the mobile sensor to follow the mobile target
T-Drive Enhancing Driving Directions with Taxi Drivers’ Intelligence
ABSTRACT: This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers’ intelligence in choosing driving directions in the physical world.
We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots.
Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations.
As a result, 60- 70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches
Toward Privacy Preserving and Collusion Resistancein a Location Proof Updating System
ABSTRACT:
Todays location-sensitive service relies on users mobile device to determine the current location. This allows malicious users to access a restricted resource or provide bogus alibis by cheating on their locations.
To address this issue, we propose A Privacy-Preserving Location proof Updating System (APPLAUS) in which colocated Bluetooth enabled mobile devices mutually generate location proofs and send updates to a location proof server. Periodically changed pseudonyms are used by the mobile devices to protect source location privacy from each other, and from the untrusted location proof server.
We develop user-centric location privacy model in which individual users evaluate their location privacy levels and decide whether and when to accept the location proof requests. In order to defend against colluding attacks, we also present betweenness ranking-based and correlation clustering-based approaches for outlier detection.
APPLAUS can be implemented with existingnetwork infrastructure, and can be easily deployed in Bluetooth enabled mobile devices with little computation or power cost. Extensive experimental results show that APPLAUS can effectively provide location proofs, significantly preserve the source location privacy, and effectively detect colluding attacks.
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