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Improving Users’ Trust Through Friendly Privacy Policies: An Empirical Study

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Risks and Security of Internet and Systems (CRiSIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9572))

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Abstract

Trust is a crucial factor for the information sharing continuance on the Internet, such as on social networks and e-commerce websites. Various studies show that many users do not trust the websites with respect to the use of their private data. Similarly, they find it unfair that their data is used to generate revenue by online service without their knowledge or without their earning from this.

In this paper, we take as main assumptions that the control of their private data as well as caring about user interests would restore their trust. Based on an empirical model, we conducted a study of user trust by offering them the possibility to adhere to a new model of privacy policy. Conventional privacy policies confront the user with a dilemma of either fully accepting all the terms and losing control of their data, or rejecting the content of the policy and not being allowed access to the service. The privacy policy presented in this paper allows users to manage access and use of their data, and also to be rewarded.

The results indicate that caring about user interests is the main factor influencing trust. Private data Control, which is greatly influenced by the management of their data by users, also has a considerable effect on trust. Results also show that users are more willing to get control over their data rather than to be rewarded for disclosing them, when they really understand how these data will be used.

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Notes

  1. 1.

    http://www.statista.com, accessed on 12 May 2015.

  2. 2.

    State of Privacy Report, Symantec, 2015.

  3. 3.

    PCAST President’s Council of Advisors on Science and Technology, Report to the president big data and privacy a technological perspective, 2014.

  4. 4.

    http://www.etrust.pro/p_intro.html, 2015.

  5. 5.

    https://www.privacyfix.com/.

  6. 6.

    European Commission, Take Control of Your Personal Data, 2012.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Operations Research, University of Montreal, Montreal, Canada

    Oluwa Lawani & Esma Aïmeur

  2. School of Information Studies, McGill University, Montreal, Canada

    Kimiz Dalkir

Authors
  1. Oluwa Lawani
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  2. Esma Aïmeur
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  3. Kimiz Dalkir
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Corresponding author

Correspondence to Oluwa Lawani .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Costas Lambrinoudakis

  2. Université de la Polynésie Française, Faa'a, French Polynesia

    Alban Gabillon

Appendices

Appendix A

Your email address

Ikrani can compare your email address and those of your contacts provided to a website or a third party online service to see if you can benefit from the combined offering.

Mark only one oval.

  • I agree (I earn 100 points)

  • I do not agree (0 point)

Your Geolocation

We use services from other companies to help us derive a general geographic area based on your IP address in order to customize certain features or deals in your area.

Mark only one oval.

  • I agree (I earn 100 points)

  • I do not agree: I do not want any geolocation, even If it meant I would not benefit from a better quality of service (0 points)

  • I do not agree: I wish my physical location to be collected only when I give my explicit agreement and only to be used for my service (I lose 100 points)

Demographics

We can obtain additional information about you, such as demographic data we purchase from other companies.

Mark only one oval.

  • I agree (I earn 100 points)

  • I do not agree (0 point)

Email content

Our automated systems analyse your content (including e-mail) to offer customized product features such as personalized search results, customized advertisements and spam detection and malware.

Mark only one oval.

  • I agree (I earn 100 points)

  • I do not agree: I do not want any analysis of my emails (0 point)

  • I do not agree: I want the analysis of my emails to only be used to detect spam and malware (I lose 100 points)

Appendix B

Trust TRU (adapted from [28])

TRU1: This online service is trustworthy.

TRU2: This online service will keep their commitments.

Benevolence (adapted from [22])

BEN1: Through this privacy policy, I feel close to the online service.

BEN2: Through this privacy policy, I think this online service cares about my concerns.

BEN3: This online service keeps customers’ interests in mind.

Private data control

PDC1: I know that my private data will not be disclosed to a third party without my permission.

PDC2: I can decide who has access to my private data.

PDC3: I can change my mind about my privacy settings whenever I want.

Management (adapted from [23])

MAN1: This online service allows me to choose which data I want to share.

MAN2: This online service offers multiple choices on each of the terms of the privacy policy.

Comprehension (adapted from [26])

COM1: The content of this policy makes sense to me.

COM2: Important information is easily identifiable.

COM3: I understand all the issues related to my privacy.

Profit

PRO1: This privacy policy allows me to get benefits.

PRO2: Incentives motivate me to deal with the online service.

Readability (adapted from [26])

REA1: Important points of this privacy policy are easily remembered.

REA2: This privacy policy can be read quickly.

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© 2016 Springer International Publishing Switzerland

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Lawani, O., Aïmeur, E., Dalkir, K. (2016). Improving Users’ Trust Through Friendly Privacy Policies: An Empirical Study. In: Lambrinoudakis, C., Gabillon, A. (eds) Risks and Security of Internet and Systems. CRiSIS 2015. Lecture Notes in Computer Science(), vol 9572. Springer, Cham. https://doi.org/10.1007/978-3-319-31811-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-31811-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31810-3

  • Online ISBN: 978-3-319-31811-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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