Adversarial Learning of Privacy-Preserving and Task-Oriented Representations

Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems. For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks. Our work aims at learning a privacy-preserving and task-oriented representation to defend against such model inversion attacks. Specifically, we propose an adversarial reconstruction learning framework that prevents the latent representations decoded into original input data. By simulating the expected behavior of adversary, our framework is realized by minimizing the negative pixel reconstruction loss or the negative feature reconstruction (i.e., perceptual distance) loss. We validate the proposed method on face attribute prediction, showing that our method allows protecting visual privacy with a small decrease in utility performance. In addition, we show the utility-privacy trade-off with different choices of hyperparameter for negative perceptual distance loss at training, allowing service providers to determine the right level of privacy-protection with a certain utility performance. Moreover, we provide an extensive study with different selections of features, tasks, and the data to further analyze their influence on privacy protection

Biometrics in schools: the role of authentic and inauthentic social transactions

Biometrics have always been part of the social world, but it is only recently that we have moved from an instinctive human model of recognition to a digital one. Recent scientific developments in the field have been capitalised upon by the commercial sector and exploited in various respects by school administration systems, with biometrics becoming comparatively widespread in UK and US schools. This brings both advantages and disadvantages as biometrics begin to change the fundamental relationship between institutions and the children in their care. This paper discusses the current state of research in terms of biometrics and social identity, the impact of commercial pressures to adopt biometric systems, and the growing relationship with data privacy issues. It analyses potential problems surrounding unproblematic adoption, and discusses how this might inform future data privacy policies. Additionally, in the paper, I identify three key social issues relating to biometric use in schools, and offer a theory of social exchange, building on the work of Homans. This includes a classification of authentic versus inauthentic transations, in the democratic sense. Finally, the paper identifies biometrics as an area of social (and legal) risk for the future

Biometrics in Schools

Biometrics have always been part of the social world, but it is only recently that we have moved from an instinctive human model to a digital one. Anyone who has been a school student will be aware that, along with eyes in the back of their heads, teachers are supposed to have a mythical sixth sense that means they are able to smell potential trouble a mile off, or identify potential culprits by individual gaits as they attempt to escape. Teachers have also long been regarded as societal experts in identifying homework and examination cheating, in the form of informally analysing patterns of handwriting and pencil use, as those of us who have attended school ourselves may recall. However identification techniques in school are currently in a process of being corporatised and commoditised, with biometric technologies being at the forefront of these developments. This chapter discusses the social and theoretical context for such change