scholarly journals A Privacy-Preserving Mobile LBS System for Small Businesses

2021 ◽  
Author(s):  
Ahmed Ahmed
Keyword(s):  
Small Businesses ◽  
Privacy Preserving ◽  
Mobile User ◽  
Mobile App ◽  
Mobile Users ◽  
Bounded Perturbation ◽  
Finite State ◽  
Job Openings ◽  
And Performance ◽  
Medium Businesses

Small-to-medium businesses are always seeking affordable ways to advertise their products and services securely. With the emergence of mobile technology, it is possible than ever to implement innovative Location-based Advertising (LBS) systems using smartphones that preserve the privacy of mobile users. In this paper, we present a prototype implementation of such systems by developing a distributed privacy-preserving system, which has parts executing on smartphones as a mobile app, as well as a web-based application hosted on the cloud. The mobile app leverages Google Maps libraries to enhance the user experience in using the app. Mobile users can use the app to commute to their daily destinations while viewing relevant ads such as job openings in their neighborhood, discounts on favorite meals, etc. We developed a client-server privacy architecture that anonymizes the mobile user trajectories using a bounded perturbation strategy. A multi-modal sensing approach is proposed for modeling the context switching of the developed LBS system, which we represent as a Finite State Machine (FSM) model. The multi-modal sensing approach can reduce the power consumed by mobile devices by automatically detecting sensing mode changes to avoid unnecessary sensing. The developed LBS system is organized into two parts: the business side and the user side. First, the business side allows business owners to create new ads by providing the ad details, Geo-location, photos, and any other instructions. Second, the user side allows mobile users to navigate through the map to see ads while walking, driving, bicycling, or quietly sitting in their offices. Experimental results are presented to demonstrate the scalability and performance of the mobile side. Our experimental evaluation demonstrates that the mobile app incurs low processing overhead and consequently has a small energy footprint.

User Perceptions of the Security of Mobile Applications

2020 ◽  
Vol 12 (4) ◽  
pp. 24-41
Author(s):  
Yasmeen Elsantil
Keyword(s):  
Private Information ◽  
Mobile Application ◽  
Mobile Apps ◽  
Mobile User ◽  
Mobile App ◽  
User Preferences ◽  
Mobile Users ◽  
User Perceptions ◽  
Perceptions Of Risk ◽  
Potential Risks

With the exponential increase in the use of mobile devices across the globe, there is a concomitant need to understand how mobile users perceive the security of mobile application, and the potential risks involved in accessing and downloading them. Such an understanding will enable users to ensure the apps they download are secure and create greater awareness in the marketplace of the presence of hackers and malware used to invade the privacy and personal details of smartphones users. Research on the perception of users' mobile security is very limited and needs further investigation. This study aims to identify how mobile users perceive the security of different mobile apps and the extent to which different apps affect such perceptions. This study also investigates mobile user preferences for the places where they can access apps and their perceptions of risk at marketplaces vs. websites. This study is based on a qualitative research in which interviews were conducted with 32 university students. The study found that mobile users do not feel secure when installing mobile apps, and that concerns about hacking personal and private information are pervasive. Users expressed more security concerns regarding entertainment apps such as games and communication rather than financial apps, such as banking. The study also found that users prefer installing apps from app stores. The findings of this research contribute a greater understanding of how mobile users perceive mobile app security and offers insights that will help developers adjust their security policies to ensure users' security. The study also presents theoretical and empirical contributions, along with limitations and suggestions for further work.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

Examining frustration and performance when priming user expectations and providing a technology malfunction

2020 ◽  
Vol 64 (1) ◽  
pp. 1846-1850
Author(s):  
Nina Ferreri ◽  
Christopher B. Mayhorn
Keyword(s):  
Augmented Reality ◽  
Technology Acceptance ◽  
Digital Technology ◽  
Mobile App ◽  
Performance Level ◽  
Qr Code ◽  
Attitudes And Behaviors ◽  
Main Effect ◽  
User Expectations ◽  
And Performance

As digital technology develops, users create expectations for performance that may be violated when malfunctions occur. This project examined how priming expectations of technology performance (high v. low v. no) and experiences of technology malfunction (present v. not present) can influence feelings of frustration and performance on a task. A preliminary sample of 42 undergraduate participants completed a QR code scavenger hunt using the augmented reality mobile app, ARIS. Following the task, participants reported what they found for each scavenger hunt clue, their responses to failures in digital technology, and technology acceptance attitudes. Several factorial ANOVAs revealed a main effect for expectation on adaptive items of the RFDT scale and a main effect for malfunction on performance level. This suggests a potential contradiction between attitudes and behaviors when considering a common scenario involving technology.

scholarly journals Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

2021 ◽  
Author(s):  
Linlin Zhang ◽  
Zehui Zhang ◽  
Cong Guan
Keyword(s):  
Convergence Rate ◽  
Performance Improvement ◽  
Gradient Descent ◽  
Homomorphic Encryption ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption ◽  
Physical Systems ◽  
Cloud Server ◽  
And Performance

AbstractFederated learning (FL) is a distributed learning approach, which allows the distributed computing nodes to collaboratively develop a global model while keeping their data locally. However, the issues of privacy-preserving and performance improvement hinder the applications of the FL in the industrial cyber-physical systems (ICPSs). In this work, we propose a privacy-preserving momentum FL approach, named PMFL, which uses the momentum term to accelerate the model convergence rate during the training process. Furthermore, a fully homomorphic encryption scheme CKKS is adopted to encrypt the gradient parameters of the industrial agents’ models for preserving their local privacy information. In particular, the cloud server calculates the global encrypted momentum term by utilizing the encrypted gradients based on the momentum gradient descent optimization algorithm (MGD). The performance of the proposed PMFL is evaluated on two common deep learning datasets, i.e., MNIST and Fashion-MNIST. Theoretical analysis and experiment results confirm that the proposed approach can improve the convergence rate while preserving the privacy information of the industrial agents.

Mobile User Data Mining and Its Applications

2011 ◽  
pp. 216-232
Author(s):  
J. Goh
Keyword(s):  
Data Mining ◽  
Potential Application ◽  
Mobile User ◽  
Current Status ◽  
Mobile Users ◽  
Privacy And Security ◽  
User Data ◽  
Developed Nations ◽  
Use Of Knowledge ◽  
Very High

Mobile user data mining is the process of extracting interesting knowledge from data collected from mobile users through various data mining methodologies. As technology progresses, and the current status of mobile phone adoption being very high in developed nations, along with improvements on mobile phones with new capabilities, it represents a strategic place for mobile user data mining. With such advanced mobile devices, locations that mobile users visit, time of communications, parties of communications, description of surrounding locations of mobile users can be gathered, stored and delivered by the mobile user to a central location, in which it have the great potential application in industries such as marketing, retail and banking. This chapter provides a general introduction on mobile user data mining followed by their potential application. As the life of mobile users are mined, general patterns and knowledge such as the sequence of locations they tend to visit, groups of people that they tends to meet, and timing where they generally active can be gathered. This supports marketing, retail and banking systems through the use of knowledge of behavior of mobile users. However, challenges such as privacy and security are still a main issue before mobile user data mining can be implemented.

scholarly journals Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 207 ◽  
Author(s):  
Elias Dritsas ◽  
Maria Trigka ◽  
Panagiotis Gerolymatos ◽  
Spyros Sioutas 
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Motion Vector ◽  
Research Work ◽  
Privacy Preserving ◽  
Mobile Users ◽  
Trajectory Clustering ◽  
K Nearest Neighbor ◽  
Trajectory Data ◽  
Spatiotemporal Databases

In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data. The k-anonymity set consists of the k nearest neighbors. We constructed a motion vector of the form (x,y,g,v) where x and y are the spatial coordinates, g is the angle direction, and v is the velocity of mobile users, and studied the problem in four-dimensional space. We followed two approaches. The former applied only k-Nearest Neighbor (k-NN) algorithm on the whole dataset, while the latter combined trajectory clustering, based on K-means, with k-NN. Actually, it applied k-NN inside a cluster of mobile users with similar motion pattern (g,v). We defined a metric, called vulnerability, that measures the rate at which k-NNs are varying. This metric varies from 1 k (high robustness) to 1 (low robustness) and represents the probability the real identity of a mobile user being discovered from a potential attacker. The aim of this work was to prove that, with high probability, the above rate tends to a number very close to 1 k in clustering method, which means that the k-anonymity is highly preserved. Through experiments on real spatial datasets, we evaluated the anonymity robustness, the so-called vulnerability, of the proposed method.

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