Privacy-aware smart city: A case study in collaborative filtering recommender systems

Zhang, Feng; Lee, Victor E.; Jin, Ruoming; Garg, Saurabh; Choo, Kim‐Kwang Raymond; Maasberg, Michele; Dong, Lijun; Cheng, Chunfu

doi:10.1016/j.jpdc.2017.12.015

Cited by 37 publications

(23 citation statements)

References 57 publications

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“…Smart cities' critique frequently deals with opposing perspectives, namely techno centred vs citizen centred or the private interest vs public interest; or long term effects and objectives, namely sustainability (Martin et al 2018); ethical issues such as privacy (Zhang et al 2019), big data; freedom (Vanolo 2014) or participation (Cardullo and Kitchin 2019). Many of these critical perspectives converge on an essential question: what does it mean to be a citizen in a city, i.e.…”

Section: From the Smart City To The Co-intelligent Territorymentioning

confidence: 99%

City Rankings and the Citizens: Exposing Representational and Participatory Gaps

Melo

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A critical reflection on the purposes, role and performance of city rankings through an holistic communicational approach is at the core of this article. Grounded on a conceptual framework that highlights the contemporary idea of the city-beyond the smart city and more as a co-intelligent, collaborative and co-creative entity, and on the performance outputs of city rankings as territorial and strategic communication tools that actually represent the state of cities, we address the citizens' presence or contribute-as main city stakeholders-to city rankings. In order to make research tangible with a practical component, an exploratory comparative content analysis of three recognized city rankings: the CBI -City Brands Index 2017, the GCR -2018 Global Cities Report, and the Global Liveability Index 2018-was carried out.Conclusive notes argue that in order to effectively represent cities, as they are lived, thought and built by their citizens in their everyday, city rankings must rely in more real-time, updated, people's perception centred data, and embed more citizen participation and insights. Moreover, methodology transparency and accountability should be promoted in order to add trust value to city rankings.

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Section: From the Smart City To The Co-intelligent Territorymentioning

confidence: 99%

City Rankings and the Citizens: Exposing Representational and Participatory Gaps

Melo

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Shehada et al [30] proposed Secure Mobile Agent Protocol (SMAP), which provides confidentiality and integrity for a mobile agent to protect vehicular communication systems in smart cities from security attacks. A privacy-preserving rating data publishing model was introduced by Zhang et al [31] to ensure privacy in recommender systems in smart cities. A trust strategy called data trustworthiness enhanced crowdsourcing strategy (DTCS) proposed a method for trust relationship establishment to enhance data trustworthiness in a mobile crowdsourcing system in smart cities [8].…”

Section: Quality Requirements In Smart Citiesmentioning

confidence: 99%

Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

2019

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It is critical for quality requirements, such as trust, privacy, and confidentiality, to be fulfilled during the execution of smart city applications. In this study, smart city applications were modeled as agent systems composed of many agents, each with its own privacy and confidentiality properties. Violations of those properties may occur during execution due to the dynamic of agent behavior, decision-making capabilities, and social activities. In this research, a framework called Agent Quality Management was proposed and implemented to manage agent quality in agent systems. This paper demonstrates the effectiveness of the approach by applying it toward a smart city application called a crowdsourced navigation system to verify and assess agent data confidentiality. The AnyLogic Agent-Based Modeling tool was used to model and conduct the experiments. The experiments showed that the framework helped to improve the detection of agent quality violations in a dynamic smart city application. The results can be further analyzed using advanced data analytic approach to reduce future violations and improve data confidentiality in a smart city environment.

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“…However, in social sensing environments, current candidate items for personalization belong to multiple kinds; therefore, adopting trust-based approaches is hard to achieve with high prediction accuracy and can raise the privacy intrusion caused by data collection, which leads to less data input to the personalization algorithm and increased user privacy concerns [8]. As a result, privacy intrusion has limited the further development of trust-aware social sensing in related research fields [9,10]; however, the possible enhancement from core users via social trust relationships provides a potential solution to this problem, which motivates our work.…”

Section: Introductionmentioning

confidence: 99%

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

Zhang

2018

Sensors

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Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.

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Privacy-aware smart city: A case study in collaborative filtering recommender systems

Cited by 37 publications

References 57 publications

City Rankings and the Citizens: Exposing Representational and Participatory Gaps

City Rankings and the Citizens: Exposing Representational and Participatory Gaps

Improving Agent Quality in Dynamic Smart Cities by Implementing an Agent Quality Management Framework

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

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