A privacy-preserving socio-demographic enrichment framework for big data and its empirical application

点击翻译按钮获取中文摘要

Recent decades have seen a substantial growth in the awareness and demand for privacy preservation set in legislation and by the public. This can be partly attributed to the proliferation of information and communications technologies, which generates large amounts of data (i.e. big data) when used. In parallel, the growing availability of big data has created opportunities for their applications, drawing upon the insights they can provide insights into people's daily behaviour patterns. However, the applicability of anonymous big data has been limited in behaviour-based analysis because of the essential role of socio-demographic information as exogenous determinants of human behaviour. Therefore, a plethora of studies have emerged to predict the absent socio-demographic attributes of respondents in various big data sources, termed the socio-demographic enrichment of big data.Existing socio-demographic enrichment approaches use either performance-based data mining and machine learning methods or statistical-fit-oriented models, which typically lack theoretical underpinnings that can explain or justify the postulated relationship between respondents' behaviour patterns (input features) and their socio-demographic attributes (output of the enrichment). A theoretical underpinning is, however, crucial because microeconomic consumer theory suggests that people's behaviour is driven by their socio-demographic attributes. One immediate consequence to neglect the underpinning microeconomic and/or sociological behavioural theoriesconcerns the incapability of existing methods to either predict the quality of enrichment or interpret the change in their performance due to the variation in data distributions. This motivates my PhD research in which I propose and formalise a new enrichment framework, called the Inverse Discrete Choice Modelling (IDCM) framework. The IDCM socio-demographic enrichment framework allows to quantitatively understand the trade-offs between enrichment accuracy and privacy preservation. Specifically, the IDCM approach performs statistical inversion to a discrete choice model (DCM), which is a well-established modelling technique relying on explicit behavioural assumptions grounded in social science, including microeconomics, sociology and psychology. The IDCM performance theory is established to estimate the IDCM enrichment performance based on known information about the data distribution in the enriched sample. This is enabled by drawing an analogy of human behaviour in information theory, i.e. observed individual as a 'message' transmitted over an information communication channel, which allows to use several powerful information-theoretic concepts to mathematically link how well we can predict who the person is and his/her privacy.So far, the ability of the IDCM performance theory is developed for socio-demographic enrichment of observation of a single, binary behaviour feature. To improve the empirical enrichment performance, the aim of the proposed research project is to extend the current IDCM approach by including multiple behaviour patterns as the input features. This can be achieved by using several DCMs that respectively captures the relationship between each behaviour feature and the enriched attribute and then to find the value of the socio-demographic attribute that is most likely to result in the joint behaviour patterns. The proposed extension of the IDCM approach involves the incorporation of machine learning or deep learning algorithms, applied to extract meaningful behaviour patterns, from raw big data, that can be further employed as the input feature for the subsequent IDCM enrichment. Correspondingly, the accompanying IDCM performance theory will be extended accordingly to accommodate the estimation of the enrichment performance based on the use of multiple behaviour features to retain transferability of the proposed extension of the IDCM methodology.