Data analytics in a privacy-concerned world

Wieringa, Jaap E.; Kannan, P.K.; Ma, Xiao; Reutterer, Thomas; Risselada, Hans; Skiera, Bernd

doi:10.1016/j.jbusres.2019.05.005

Cited by 85 publications

(50 citation statements)

References 39 publications

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“…The k-anonymity [10] protects users' privacy by placing at least k users in an equivalence class with same QI's values. Hence, the probability of reidentifying someone from anonymized data becomes 1 k . Due to the conceptual simplicity, this model has been widely used in PPDP of SN users [37][38][39][40].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Aside from the required information necessary to create an account, most users readily post other valuable data including popular songs, favourite talk shows, political and religious views, and interesting movies which third parties and analysts collect. Accordingly, this goldmine of data has attracted the attention of interested third parties who are interested in such data as part of their business strategy [1]. Such data is invaluable for understanding the social trends, users' behaviours, opinions, contents recommendation, and targeted advertisement [2].…”

Section: Introductionmentioning

confidence: 99%

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Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users' data publishing because some attributes may expose multiple users' identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users' communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users' community can occur from the privacy preserved published data. Each item of user attributes impacts users' community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users' unique identifications and sensitive information inferences more easily, and their presence in published data increases users' community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user's community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users' community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user's dataset to effectively preserve users' community privacy. Data generalization is performed adaptively by considering both user attributes' susceptibility and entropy simultaneously. The proposed algorithm controls overgeneralization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users' community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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“…[97]. Analytics firms are heavily relying the SN user data for analyzing social trends, user attitudes towards latest brands, intent mining, sentiment analysis, and personality analysis [98]. The related concepts, applications, uses, and technological advancements of SN are comprehensively explained in studies [99][100][101][102][103].…”

Section: Applications Of the Graph Theory In Social Network (Sn)mentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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“…"Data is the new oil" [6][7][8] is one of many statements that recognizes the rising importance of data and the urgent need to process it smartly, to extract useful knowledge from it, and more importantly, to build the most suitable data models.…”

Section: Traffic Modeling and Its Benefitsmentioning

confidence: 99%

“…At the initial date t = t 0 , a batch is measured by sensor nodes at section R 1 , and there is a creation of a batch Cβ 1 (t 0 ) = (6,19,0,40) according to Definition 2. The state of the created batch Cβ 1 is free and flows freely along the stretch since its actual density is less than the critical density (d cri i = 50, d i = 19) as described in Section 4.3.…”

Section: Definition 2 (Controllable Batch) a Controllable Batch Denmentioning

confidence: 99%

An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

Riouali

Benhlima

Bah

2019

JSAN

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The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements.

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Data analytics in a privacy-concerned world

Cited by 85 publications

References 39 publications

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

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