SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication

Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive attributes, can result in privacy leakage. To enable data analysis, a strong notion of privacy is required to avoid risking unintended privacy violations.Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Despite this trade-off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade-off and how to address it appropriately. Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we identify trends and connections in the contributions to the field of differential privacy for histograms and synthetic data and 2) we provide an understanding of the privacy/accuracy trade-off challenge by crystallizing different dimensions to accuracy improvement. Accordingly, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy

Survey: Leakage and Privacy at Inference Time

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malevolent leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malevolent leakage, available defences, followed by the currently available assessment metrics and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research

Алгоритмічно-програмний метод шифрування даних з використанням нейронних мереж

Дана магістерська дисертація присвячена розробленню алгоритмічно-програмного методу шифрування даних з використанням нейронних мереж. У роботі здійснено порівняльний аналіз методів захисту приватних наборів даних, які можуть бути використані при побудові систем аналізу даних і штучного інтелекту, а також проведено докладний аналіз моделі шифрування, яка використовує генеративні конкуруючі нейронні мережі: досліджено її архітектуру, функції втрат та гіперпараметри моделі. Запропоновано метод шифрування наборів даних з використанням нейронних мереж та модифікацію моделі шифрування. Розроблено програмну систему, яка реалізує запропонований метод шифрування даних, і дозволяє здійснювати класифікацію як оригінальних, так і зашифрованих даних. У роботі було отримано експериментальні результати роботи запропонованого методу й модифікованої моделі шифрування даних, а також класифікації оригінальних та зашифрованих даних.This master's thesis is devoted to the development of algorithmic-software method of data encryption using neural networks. The paper compares the methods of protection of private data sets that can be used in the construction of data analysis and artificial intelligence systems, as well as a detailed analysis of the encryption model that uses generative adversarial neural networks: its architecture, loss functions and hyperparameters of the model. A method of encrypting datasets using neural networks and a modification of the encryption model are proposed. A software system is developed that implements the proposed data encryption method and allows the classification of both original and encrypted data. The experimental results of the proposed method and the modified model of data encryption, as well as the classification of original and encrypted data were obtained