Abstract
Small pieces of data that are shared online, over time and across multiple social networks, have the potential to reveal more cumulatively than a person intends.
This could result in harm, loss or detriment to them depending what information is revealed, who can access it, and how it is processed. But how aware are social network users of how much information they are actually disclosing? And if they could examine all their data, what cumulative revelations might be found that could potentially increase their risk of various online threats (social engineering, fraud, identify theft, loss of face, etc.)? In this paper, we present DataMirror, an initial prototype tool, that enables social network users to aggregate their online data so that they can search, browse and visualise what they have put online. The aim of the tool is to investigate and explore people's awareness of their data self that is projected online; not only in terms of the volume of information that they might share, but what it may mean when combined together, what pieces of sensitive information may be gleaned from their data, and what machine learning may infer about them given their data.
This could result in harm, loss or detriment to them depending what information is revealed, who can access it, and how it is processed. But how aware are social network users of how much information they are actually disclosing? And if they could examine all their data, what cumulative revelations might be found that could potentially increase their risk of various online threats (social engineering, fraud, identify theft, loss of face, etc.)? In this paper, we present DataMirror, an initial prototype tool, that enables social network users to aggregate their online data so that they can search, browse and visualise what they have put online. The aim of the tool is to investigate and explore people's awareness of their data self that is projected online; not only in terms of the volume of information that they might share, but what it may mean when combined together, what pieces of sensitive information may be gleaned from their data, and what machine learning may infer about them given their data.
| Original language | English |
|---|---|
| Title of host publication | SIGIR '20 - The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Pages | 2125-2128 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 25 Jul 2020 |