Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial

Tuovinen, Lauri; Smeaton, Alan F.

doi:10.1371/journal.pone.0265997

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…Such data is collected by an individual and is not normally shared with others or made public. Lifelogs represent a personal record that may be analysed either directly by the individual gathering the data, or by others on their behalf [ 24 ]. This is done in order to observe long-term behavioural patterns and changes in terms of health, well-being or cognitive changes, as well as to facilitate retrieval of information from the individual’s past [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Calculating the matrix profile from noisy data

Hehir,

Smeaton

2023

PLoS ONE

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The matrix profile (MP) is a data structure computed from a time series which encodes the data required to locate motifs and discords, corresponding to recurring patterns and outliers respectively. When the time series contains noisy data then the conventional approach is to pre-filter it in order to remove noise but this cannot apply in unsupervised settings where patterns and outliers are not annotated. The resilience of the algorithm used to generate the MP when faced with noisy data remains unknown. We measure the similarities between the MP from original time series data with MPs generated from the same data with noisy data added under a range of parameter settings including adding duplicates and adding irrelevant data. We use three real world data sets drawn from diverse domains for these experiments Based on dissimilarities between the MPs, our results suggest that MP generation is resilient to a small amount of noise being introduced into the data but as the amount of noise increases this reslience disappears.

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Section: Methodsmentioning

confidence: 99%

Calculating the matrix profile from noisy data

Hehir,

Smeaton

2023

PLoS ONE

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“…An alternative approach to allowing users to explore their own personal information drawn from across individual sources is our proof of concept work described in [15,14]. Here, the data owner collaborates with a data analysis expert where they find one another, communicate and share datasets and analysis results with one another in a secure and anonymised way.…”

Section: Problems Challenges and Opportunities For Personal Informationmentioning

confidence: 99%

Managing Personal Information

Smeaton

2023

Lecture Notes in Computer Science

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There is an increasing awareness of the potential that our own self-gathered personal information has for our wellness and our health. This is partly because of our increasing awareness of what others -the major internet companies mainly -have been able to do with the personal information that they gather about us. The biggest hurdle to us using and usefully exploiting our own self-gathered personal data are the applications to support that. In this paper we highlight both the potential and the challenges associated with more widespread use of our own personal data by ourselves and we point at ways in which we believe this might happen. We use the work done in lifelogging and the annual Lifelog Search Challenge as an indicator of what we can do with our own data. We review the small number of existing systems which do allow aggregation of our own personal information and show how the use of large language models could make the management of our personal data more straightforward.

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