Investigating Factors Affecting Personal Data Disclosure

Perentis, Christos; Vescovi, Michele; Lepri, Bruno

doi:10.1145/2740908.2742757

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…The scarcity of users' specific preference and choice data is a common problem of e-commerce applications and RSs in particular; users may have not consistently interacted with the RS and, even if they have a large interaction history, they may not be eager to disclose it. In travel and tourism applications some travellers consider this information personal and private, especially because it can reveal their past position (Smith et al 1996;Perentis et al 2015;Poikela et al 2014).…”

Section: Clustering Trajectoriesmentioning

confidence: 99%

“…In fact, we do not want to suggest as next-POI any item that the user has already visited. Despite this, it is important to highlight that the user may still find in the recommendations some POIs that she has already visited, because she may have not indicated in the first phase of the user-system all the POIS that she visited in the past (Smith et al 1996;Perentis et al 2015).…”

Section: Recommendationsmentioning

confidence: 99%

“…Q-BASE models with a reward function how a POI is estimated to be relevant for a user and recommends POI-visits that are estimated to give a large reward. Moreover, we observe that users typically provide scarce and not explicit feedback (e.g., ratings) and are not keen to disclose behavioural data (Smith et al 1996;Perentis et al 2015). Hence, Q-BASE learns the reward function by applying Inverse Reinforcement 1 http:// www.…”

Section: Introductionmentioning

confidence: 99%

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Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

Massimo

Ricci

2021

Inf Technol Tourism

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Recommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user’s ratings or choices. But, when a precise RS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user’s observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called and follow a classical Nearest Neighbour approach, while the other two, and are based on Inverse Reinforcement Learning. and optimise precision, tries to identify the characteristics of POIs that make them relevant, and , a novel RS here introduced, is similar to but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by and optimise precision essentially by recommending quite popular POIs. can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of and are liked more than those produced by . The rationale of that was found in the large percentage of novel recommendations produced by , which are difficult to appreciate. However, excels in recommending items that are both novel and liked by the users.

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Section: Clustering Trajectoriesmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

Massimo

Ricci

2021

Inf Technol Tourism

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“…Some recent works [33,34] have also investigated the factors affecting users choices toward the disclosure of their personal data. Their works are different from our work, which considered the factors from the perspectives of data features and sampling rates.…”

Section: Related Workmentioning

confidence: 99%

An information-aware visualization for privacy-preserving accelerometer data sharing

Xiao

Zhao

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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In the age of big data, plenty of valuable sensing data have been shared to enhance scientific innovation. However, this may cause unexpected privacy leakage. Although numerous privacy preservation techniques, such as perturbation, encryption, and anonymization, have been proposed to conceal sensitive information, it is usually at the cost of the application utility. Moreover, most of the existing works did not distinguished the underlying factors, such as data features and sampling rate, which contribute differently to utility and privacy information implied in the shared data. To well balance the application utility and privacy leakage for data sharing, we utilize mutual information and visualization techniques to analyze the impact of the underlying factors on utility and privacy, respectively, and design an interactive visualization tool to help users identify the appropriate solution to achieve the objectives of high application utility and low privacy leakage simultaneously. To illustrate the effectiveness of the proposed scheme and tool, accelerometer data collected from mobile devices have been adopted as an illustrative example. Experimental study has shown that feature selection and sampling frequency play dominant roles in reducing privacy leakage with much less reduction on utility, and the proposed visualization tool can effectively recommend the appropriate combination of features and sampling rates that can help users make decision on the trade-off between utility and privacy.

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“…This is a consequence of the increased public attention the privacy concerns and issues receive. These concerns arise mainly from the increasing ubiquitous collection of personal data and from the unprecedented daily privacy challenges users face making constantly decisions between service usage and data protection [Perentis et al 2015]. Such concerns have been recently taken into account by policy-makers, as shown by the new EU directive for privacy [Council 2016].…”

Section: Practical Implicationsmentioning

confidence: 99%

Anonymous or not? Understanding the Factors Affecting Personal Mobile Data Disclosure

Perentis¹,

Vescovi²,

Leonardi³

et al. 2017

Preprint

Self Cite

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The wide adoption of mobile devices and social media platforms have dramatically increased the collection and sharing of personal information. More and more frequently, users are called to take decisions concerning the disclosure of their personal information. In this study, we investigate the factors affecting users' choices toward the disclosure of their personal data, including not only their demographic and self-reported individual characteristics, but also their social interactions and their mobility patterns inferred from months of mobile phone data activity. We report the findings of a field-study conducted with a community of 63 subjects provided with (i) a smart-phone and (ii) a Personal Data Store (PDS) enabling them to control the disclosure of their data. We monitor the sharing behavior of our participants through the PDS, and evaluate the contribution of different factors affecting their disclosing choices of location and social interaction data. Our analysis shows that social interaction inferred by mobile phones is an important factor revealing willingness to share, regardless of the data type. In addition, we provide further insights on the individual traits relevant to the prediction of sharing behavior.CCS Concepts: •Human-centered computing → Ubiquitous and mobile computing systems and tools; •Security and privacy → Social aspects of security and privacy;

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Investigating Factors Affecting Personal Data Disclosure

Cited by 6 publications

References 7 publications

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

An information-aware visualization for privacy-preserving accelerometer data sharing

Anonymous or not? Understanding the Factors Affecting Personal Mobile Data Disclosure

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