How did you know that about me? Protecting users against unwanted inferences
The widespread adoption of social computing applications is transforming our world. It has changed the way we routinely communicate and navigate our environment and enabled political revolutions. However, despite these applications’ ability to support social action, their use puts individual privacy...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2016-01-01
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| Series: | EAI Endorsed Transactions on Security and Safety |
| Subjects: | |
| Online Access: | http://eudl.eu/doi/10.4108/trans.sesa.2011.e3 |
| _version_ | 1818418829143834624 |
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| author | Sara Motahari Julia Mayer Quentin Jones |
| author_facet | Sara Motahari Julia Mayer Quentin Jones |
| author_sort | Sara Motahari |
| collection | DOAJ |
| description | The widespread adoption of social computing applications is transforming our world. It has changed the way we routinely communicate and navigate our environment and enabled political revolutions. However, despite these applications’ ability to support social action, their use puts individual privacy at considerable risk. This is in large part due to the fact that the public sharing of personal information through social computing applications enables potentially unwanted inferences about users’ identity, location, or other related personal information. This paper provides a systematic overview of the social inference problem. It highlights the public’s and research community’s general lack of awareness of the problem and associated risks to user privacy. A social inference risk prediction framework is presented and associated empirical studies that attest to its validity. This framework is then used to outline the major research and practical challenges that need to be addressed if we are to deploy effective social inference protection systems. Challenges examined include how to address the computational complexity of social inference risk modeling and designing user interfaces that inform users about social inference opportunities. |
| first_indexed | 2024-12-14T12:28:53Z |
| format | Article |
| id | doaj.art-a86ad54c7d3e47c1b9eca09720fe18ed |
| institution | Directory Open Access Journal |
| issn | 2032-9393 |
| language | English |
| last_indexed | 2024-12-14T12:28:53Z |
| publishDate | 2016-01-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Security and Safety |
| spelling | doaj.art-a86ad54c7d3e47c1b9eca09720fe18ed2022-12-21T23:01:14ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Security and Safety2032-93932016-01-011111310.4108/trans.sesa.2011.e3How did you know that about me? Protecting users against unwanted inferencesSara Motahari0Julia Mayer1Quentin Jones2New Jersey Institute of Technology, Newark, New Jersey, NJ 07103-3513, USA; Sara.gatmir-motahari@sprint.comNew Jersey Institute of Technology, Newark, New Jersey, NJ 07103-3513, USANew Jersey Institute of Technology, Newark, New Jersey, NJ 07103-3513, USAThe widespread adoption of social computing applications is transforming our world. It has changed the way we routinely communicate and navigate our environment and enabled political revolutions. However, despite these applications’ ability to support social action, their use puts individual privacy at considerable risk. This is in large part due to the fact that the public sharing of personal information through social computing applications enables potentially unwanted inferences about users’ identity, location, or other related personal information. This paper provides a systematic overview of the social inference problem. It highlights the public’s and research community’s general lack of awareness of the problem and associated risks to user privacy. A social inference risk prediction framework is presented and associated empirical studies that attest to its validity. This framework is then used to outline the major research and practical challenges that need to be addressed if we are to deploy effective social inference protection systems. Challenges examined include how to address the computational complexity of social inference risk modeling and designing user interfaces that inform users about social inference opportunities.http://eudl.eu/doi/10.4108/trans.sesa.2011.e3inference problemprivacysocial computingubiquitous computing |
| spellingShingle | Sara Motahari Julia Mayer Quentin Jones How did you know that about me? Protecting users against unwanted inferences EAI Endorsed Transactions on Security and Safety inference problem privacy social computing ubiquitous computing |
| title | How did you know that about me? Protecting users against unwanted inferences |
| title_full | How did you know that about me? Protecting users against unwanted inferences |
| title_fullStr | How did you know that about me? Protecting users against unwanted inferences |
| title_full_unstemmed | How did you know that about me? Protecting users against unwanted inferences |
| title_short | How did you know that about me? Protecting users against unwanted inferences |
| title_sort | how did you know that about me protecting users against unwanted inferences |
| topic | inference problem privacy social computing ubiquitous computing |
| url | http://eudl.eu/doi/10.4108/trans.sesa.2011.e3 |
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