Understanding the Valuation of Location Privacy: a Crowdsourcing-Based Approach

The exchange of private information for services or other benefits is a commonplace practice today in the advent of mobile technology. In the case of mobile services, the exchanged commodity is increasingly often spatial location of the user. To decide whether this transaction is beneficial, the user needs to evaluate the exchange value of this commodity. To assess the value users give to their location, and to understand its relationship with location sharing, we conducted a study on a mobile crowdsourcing platform (N=190). We find that users\u27 valuation of location privacy is dependent on the sharing scenario. For instance, when the location is to be shared with an untrusted advertiser, the users require a premium as compensation for their information. Additionally, benefit perception and trust are found to be connected with more frequent location sharing, while perceived risks and privacy concern are associated with sharing one’s location less frequently

ATTENUATING PERCEIVED PRIVACY RISK OF LOCATION-BASED MOBILE SERVICES

The rapid diffusion of mobile devices has spurred the development and use of location-based mobile services (LBMS). LBMS have the potential to add value to businesses through sale of LBMS applications and targeted marketing of products and services. However, studies have shown that individuals’ intention to use LBMS is plagued by the perceived privacy risks of disclosing location and personal information. This study examines how various consumption values may weaken the negative influence of perceived privacy risk on individuals’ intention to use LBMS based on the multi-dimensional concept of privacy risk, theory of consumption values, and privacy calculus. The attenuating effects of conditional, emotional, epistemic, functional, and social values are studied. Results of a survey of 194 potential users of a LBMS show that conditional, functional, and social values have significant attenuating effects. This study contributes to research by looking beyond the separate and direct effects of perceived privacy risk and consumption values to provide new insights on their joint influences. For practitioners such as LBMS providers and businesses’ marketing managers, the findings highlight the type of values that should be emphasized in designing and promoting LBMS

Improving Source Location Privacy in Social Internet of Things Using a Hybrid Phantom Routing Technique

The amalgamation of Smart IoT and Machine learning is an emerging research area. In this context, a new trend in IoT called Social IoT has been considered for this study. The Social IoT has benefits of connectivity exhibited within the network of connected objects through the Internet of Things (IoT). It covers the entire world and provides innovative services to improve life standards, establishes novel businesses, and makes buildings and cities. Certain smart things allow the collection of ubiquitous data or traffic, which pose a threat to source location privacy. Therefore, it limits the source of the Internet of Things vision if implemented wrongly. These threats come along with some challenges, adversary profiles, and the location privacy of personal data. When they are used to monitor important assets, the attacker can easily hunt the location of these assets. However, the source location constitutes a way to prevent the adversary from finding the location of the source. This research has used a hybrid phantom method by combining the phantom node and multi-path route that improves privacy and reduces the consumption of energy. The Analytic Hierarchal Process (AHP) is used for phantom node selection, based on parameters such as energy, distance, heterogeneity, and neighbor list. The result shows the average consistency value of the parameters is 4.2 and the consistency index value is 0.066. The overall priority of the alternative node is 2.089 as compared to other nodes. The sum of the vector weight value is obtained as 4.845. The total average energy consumption is 1.211 J and the average safety period capture ratio is 59.41%. The proposed techniques overwhelmed the deficiencies in existing techniques, reduces energy consumption improves the safety period and increases the network lifetime

Enhancing the security of RCIA ultra-lightweight authentication protocol by using random number generator (RNG) technique

With the growing demand for low-cost Radio Frequency Identification (RFID) system, there is a necessity to design RFID ultra-lightweight authentication protocols to be compatible with the system and also resistant against possible attacks. However, the existing ultra-lightweight authentication protocols are susceptible to wide range of attacks. This study is an attempt to enhance the security of Robust Confidentiality, Integrity, and Authentication (RCIA) ultra-lightweight authentication protocols especially with regard to privacy issue. In the RCIA protocol, IDs value is sent between reader and tag as a constant value. The constant value will enable attacker to trace the location of the tag which violates the privacy users. In order to enhance the security of RCIA protocol, Random Number Generator (RNG) technique has been used. This technique relies on generating random numbers in the tag side, based on Bitwise operations. The idea of this technique is to change the IDs of a tag on every query session so that it will not stay as a constant value. The implementation of Enhanced RCIA has been conducted by using a simulation. The simulation provided the ability to show that the operations of RCIA protocol as to compare with the enhanced RCIA. The outcome shows that the enhanced RCIA outperforms existing one in terms of privacy

A Look into User\u27s Privacy Perceptions and Data Practices of IoT Devices

Purpose: With the rapid deployment of Internet of Things (IoT) technologies, it has been essential to address the security and privacy issues through maintaining transparency in data practices. The prior research focused on identifying people’s privacy preferences in different contexts of IoT usage, and their mental models of security threats. However, there is a dearth in existing literature to understand the mismatch between user’s perceptions and the actual data practices of IoT devices. Such mismatches could lead users unknowingly sharing their private information, exposing themselves to unanticipated privacy risks. We aim to identify these mismatched privacy perceptions in our work. Methodology: We conducted a lab study with 42 participants, where we compared participants’ perceptions with the data practices stated in the privacy policy of 28 IoT devices from different categories, including health & exercise, entertainment, smart homes, toys & games, and pets. Findings: We identified the mismatched privacy perceptions of users in terms of data collection, sharing, protection, and storage period. Our findings revealed the mismatches between user’s perceptions and the data practices of IoT devices for various types of information, including personal, contact, financial, heath, location, media, connected device, online social media, and IoT device usage. Value: The findings from this study lead to our recommendations on designing simplified privacy notice by highlighting the unexpected data practices, which in turn, would contribute to the secure and privacy-preserving use of IoT devices

Improving Security and Privacy in Online Social Networks

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users\u27 private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs

Confidentiality in a Mobile Location Based Service

This thesis tackles the current issue of privacy in the growing mobile location-based applications, which provide an added value to users using their location. These location-based applications need a specific study of privacy attending to tracking, in order guarantee user right of privacy. This thesis is divided into two parts. The first discusses privacy and data security. In order to link terms data security and privacy, and overcome the differences between both fields, confidentiality is introduced. Data security ensures confidentiality, which guarantees user privacy. Since privacy is a social phenomenon, influenced by society, it will be incorporated through a survey of user opinion. Also, legislation about privacy needs to be taken into account. In the constructive part, a location-based application is built, in order to demonstrate how considering interactions of privacy, confidentiality, and data security influence a real application. A survey offered to potential users is used to define the level of privacy expected. Finally, the system is evaluated against confidentiality level established by privacy level from the survey results. In addition, this application will provide to society a tool to request help from people near the user, creating a social network based on trust in other users

Understanding the current trends in mobile crowdsensing - a business model perspective: case MyGeo Trust

Crowdsensing and personal data markets that have emerged around it have rapidly gained momentum in parallel with the appearance of mobile devices. Collecting information via mobile sensors and the applications relying on these, the privacy of mobile users can be threatened, especially in the case of location-related data. In 2015, a research project called MyGeoTrust was initiated to investigate this issue. One aim of the project was to study the potential business models for a trusted, open-source crowdsourcing platform. This study, carried within the MyGeoTrust project, reviews existing literature about business models, location-based services, and open-source software development. It then investigates the relationship between these topics and mobile crowdsensing. As a whole, this thesis provides an overview on the development of location-based services, as well as the current trends and business models in crowdsensing. The empirical part of the thesis employs embedded case study methodology, acquiring empirical data from several sources. The analyzed case is the MyGeoTrust project itself, and other empirical data is collected via market analysis, interim reports, a user survey, and semi-structured interviews. This material forms the baseline for the empirical study and project-specific recommendations. The findings suggest that creating a two- or multisided platform is the most robust business model for mobile crowdsensing. The identified benefits of platform-based business models include facilitating the value exchange between self-governing groups and possibilities to build positive network effects. This is especially the case with open-source software and open data since the key value for users - or “the crowd” in other terms - is created through network effects. In the context of open business models, strategic planning, principally licensing, plays a central role. Also, for a differentiated platform like MyGeoTrust finding the critical mass of users is crucial, in order to create an appealing alternative to current market leaders. Lastly, this study examines how transformational political or legal factors may shape the scene and create requirements for novel, privacy-perceiving solutions. In the present case study, the upcoming European Union (EU) General Data Protection Regulation (GDPR) legislation is a central example of such a factor

Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.Comment: Please contact [zhengyinhe1 at 163 dot com] for the PersonalDialog datase