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Proceedings ArticleDOI

Inferring emotional state of a user by user profiling

01 Dec 2016-pp 530-535
TL;DR: How the profile information is used to get the emotional state of a user and the main issues regarding user profiles are studied from the perspectives of these research fields are studied.
Abstract: User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields.
Topics: User profile (75%), User modeling (67%), User interface design (66%), Computer user satisfaction (65%), User journey (64%)
Citations
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Proceedings ArticleDOI
15 Jun 2020-
TL;DR: This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user’s privacy.
Abstract: Recognising user’s risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user’s privacy. This observer agent helps to create a decision-making model that influences the user’s decision following real-time threats or risky behaviours.

Cites background from "Inferring emotional state of a user..."

  • ...1) Public Layer, 2) Private Layer, 3) Personal Layer [2] [17]....

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  • ...user’s actions tends to be useful to manage user security and privacy but it also could be a significant drawback of user privacy as well [2]....

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  • ...It could obtain appropriate, adequate and accurate information about a user’s interests and characteristics and demonstrate them with minimal user intervention [2]....

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Proceedings ArticleDOI
28 Aug 2019-
TL;DR: A framework for obtaining user profile based on collaboration between smart terminal devices and cloud servers is proposed, which reduces the amount of resources consumed on users' phones, while avoiding uploading too much data to the cloud servers.
Abstract: With the increasing popularity of mobile phones, constructing user profile from the usage of mobile phone becomes a critical research interest. Previously, the key of these researches was the improvement of the accuracy of user profiling algorithms. However, most of algorithms are hard to achieve theoretical optimum under practical scenario due to the limited performance of consumer-grade terminal. A common solution is to upload raw user data to cloud server and analyze them on the cloud-side, which leads to the huge consumption of cloud computing resources. In this paper, we propose a framework for obtaining user profile based on collaboration between smart terminal devices and cloud servers. The framework divides the computational flow of the user profiling algorithm into two parts, which will be executed on the phone and on the server respectively. The framework reduces the amount of resources consumed on users' phones, while avoiding uploading too much data to the cloud servers. This paper introduces the principle and structure of the framework. Finally, the framework is compared with a terminal-side frame which perform user profile calculations only using terminal devices and a cloud frame obtaining user profile only using cloud servers.

References
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Proceedings ArticleDOI
18 May 2008-
TL;DR: This work applies the de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service, and demonstrates that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset.
Abstract: We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information

1,962 citations


"Inferring emotional state of a user..." refers background in this paper

  • ...The cloud computing security with the issues in data security and privacy protection has discussed in [10]....

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Book ChapterDOI
01 Jan 2007-
TL;DR: This chapter introduces the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings.
Abstract: One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.

1,558 citations


8


Journal ArticleDOI
TL;DR: The mix zone is introduced-a new construction inspired by anonymous communication techniques-together with metrics for assessing user anonymity, based on frequently changing pseudonyms.
Abstract: As location-aware applications begin to track our movements in the name of convenience, how can we protect our privacy? This article introduces the mix zone-a new construction inspired by anonymous communication techniques-together with metrics for assessing user anonymity. It is based on frequently changing pseudonyms.

1,498 citations


Journal ArticleDOI
TL;DR: A scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs is described, including the development of a personalized location anonymization model and a suite of location perturbation algorithms.
Abstract: Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An important challenge in the wide deployment of location-based services (LBSs) is the privacy-aware management of location information, providing safeguards for location privacy of mobile clients against vulnerabilities for abuse. This paper describes a scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs. This architecture includes the development of a personalized location anonymization model and a suite of location perturbation algorithms. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k-anonymity for a wide range of mobile clients with context-sensitive privacy requirements. This framework enables each mobile client to specify the minimum level of anonymity that it desires and the maximum temporal and spatial tolerances that it is willing to accept when requesting k-anonymity-preserving LBSs. We devise an efficient message perturbation engine to implement the proposed location privacy framework. The prototype that we develop is designed to be run by the anonymity server on a trusted platform and performs location anonymization on LBS request messages of mobile clients such as identity removal and spatio-temporal cloaking of the location information. We study the effectiveness of our location cloaking algorithms under various conditions by using realistic location data that is synthetically generated from real road maps and traffic volume data. Our experiments show that the personalized location k-anonymity model, together with our location perturbation engine, can achieve high resilience to location privacy threats without introducing any significant performance penalty.

832 citations


Proceedings ArticleDOI
23 Mar 2012-
TL;DR: This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle and describes future research work about dataSecurity and privacy Protection issues in cloud.
Abstract: It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.

583 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20201
20191