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The tradeoff between the utility and risk of location data and implications for public good
Computers and Society · Submitted 22 May 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes.
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The abstract, sentence by sentence
High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it's been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | This data poses significant security, privacy, and ethical risks: it's been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. | background 1.00 | 0.06 | 0.04 | asserted 1.00 |
| s2 | This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. | background 1.00 | 0.07 | 0.06 | asserted 1.00 |
| s3 | Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. | background 0.99 | 0.05 | 0.04 | asserted 1.00 |
| s4 | But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. | background 0.93 | 0.11 | 0.12 | asserted 1.00 |
| s5 | How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? | gap 0.69 | 0.03 | 0.03 | unclear 0.64 |
| s6 | We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. | contribution 0.93 | 0.84 | 0.06 | asserted 0.95 |
| s7 | We then hypothesize how this tradeoff differs between private market use and its potential use for public good. | contribution 0.97 | 0.39 | 0.06 | speculative 0.85 |
| s8 | We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms. | contribution 0.97 | 0.59 | 0.07 | asserted 0.97 |
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The paper
Original abstract
High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it's been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.
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How the literature uses it
3 arXiv papers cite this in 3 sentences
What it cites · 4 sentences without a confident label
Cites as support 2 papers
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With simple tooling, mobility data at this resolution is detailed enough to describe traffic flows, geographic areas that an individual frequents, and a person’s daily routines [ref] .
Data for Development: the D4D Challenge on Mobile Phone Data · Level of Aggregation · 0.98
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CDRs with this type of location data have been used in machine learning models to correctly infer the professions and unemployment status of individuals, as well as other socio economic characteristics [ref] .
Estimating individual employment status using mobile phone network data · Level of Aggregation · 0.96
Cites as background 1 paper
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There are sizable computational literatures that detail both re-identification and anonymization strategies for CDRs and location data, and they often directly or indirectly imply a relationship between utility and risk [ref] .
On the anonymizability of mobile traffic datasets · The Risk-Utility Tradeoff · 0.82
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