Interactive Range Queries under Differential Privacy

Differential privacy approaches employ a curator to control data sharing with analysts without compromising individual privacy. The curator’s role is to guard the data and determine what is appropriate for release using the parameter epsilon to adjust the accuracy of the released data. A low epsilon value provides more privacy, while a higher epsilon value is associated with higher accuracy. Counting queries, which ”count” the number of items in a dataset that meet specific conditions, impose additional restrictions on privacy protection. In particular, if the resulting counts are low, the data released is more specific and can lead to privacy loss. This work addresses privacy challenges in single-attribute counting-range queries by proposing a Workload Partitioning Mechanism (WPM) which generates estimated answers based on query sensitivity. The mechanism is then extended to handle multiple-attribute range queries by preventing interrelated attributes from revealing private information about individuals. Further, the mechanism is paired with access control to improve system privacy and security, thus illustrating its practicality. The work also extends the WPM to reduce the error to be polylogarithmic in the sensitivity degree of the issued queries. This thesis describes the research questions addressed by WPM to date, and discusses future plans to expand the current research tasks toward developing a more efficient mechanism for range queries

The Serums Tool-Chain:Ensuring Security and Privacy of Medical Data in Smart Patient-Centric Healthcare Systems

Digital technology is permeating all aspects of human society and life. This leads to humans becoming highly dependent on digital devices, including upon digital: assistance, intelligence, and decisions. A major concern of this digital dependence is the lack of human oversight or intervention in many of the ways humans use this technology. This dependence and reliance on digital technology raises concerns in how humans trust such systems, and how to ensure digital technology behaves appropriately. This works considers recent developments and projects that combine digital technology and artificial intelligence with human society. The focus is on critical scenarios where failure of digital technology can lead to significant harm or even death. We explore how to build trust for users of digital technology in such scenarios and considering many different challenges for digital technology. The approaches applied and proposed here address user trust along many dimensions and aim to build collaborative and empowering use of digital technologies in critical aspects of human society

Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data

Big data has remodeled the way organizations supervise, examine and leverage data in any industry. To safeguard sensitive data from public contraventions, several countries investigated this issue and carried out privacy protection mechanism. With the aid of quasi-identifiers privacy is not said to be preserved to a greater extent. This paper proposes a method called evolutionary tree-based quasi-identifier and federated gradient (ETQI-FD) for privacy preservations over big healthcare data. The first step involved in the ETQI-FD is learning quasi-identifiers. Learning quasi-identifiers by employing information loss function separately for categorical and numerical attributes accomplishes both the largest dissimilarities and partition without a comprehensive exploration between tuples of features or attributes. Next with the learnt quasi-identifiers, privacy preservation of data item is made by applying federated gradient arbitrary privacy preservation learning model. This model attains optimal balance between privacy and accuracy. In the federated gradient privacy preservation learning model, we evaluate the determinant of each attribute to the outputs. Then injecting Adaptive Lorentz noise to data attributes our ETQI-FD significantly minimizes the influence of noise on the final results and therefore contributing to privacy and accuracy. An experimental evaluation of ETQI-FD method achieves better accuracy and privacy than the existing methods

Private Multi-Winner Voting for Machine Learning

Private multi-winner voting is the task of revealing $k$-hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine learning literature despite its prevalence in many domains such as healthcare. We propose three new DP multi-winner mechanisms: Binary, $\tau$, and Powerset voting. Binary voting operates independently per label through composition. $\tau$ voting bounds votes optimally in their $\ell_2$ norm for tight data-independent guarantees. Powerset voting operates over the entire binary vector by viewing the possible outcomes as a power set. Our theoretical and empirical analysis shows that Binary voting can be a competitive mechanism on many tasks unless there are strong correlations between labels, in which case Powerset voting outperforms it. We use our mechanisms to enable privacy-preserving multi-label learning in the central setting by extending the canonical single-label technique: PATE. We find that our techniques outperform current state-of-the-art approaches on large, real-world healthcare data and standard multi-label benchmarks. We further enable multi-label confidential and private collaborative (CaPC) learning and show that model performance can be significantly improved in the multi-site setting.Comment: Accepted at PoPETS 202

Application Of Blockchain Technology And Integration Of Differential Privacy: Issues In E-Health Domains

A systematic and comprehensive review of critical applications of Blockchain Technology with Differential Privacy integration lies within privacy and security enhancement. This paper aims to highlight the research issues in the e-Health domain (e.g., EMR) and to review the current research directions in Differential Privacy integration with Blockchain Technology.Firstly, the current state of concerns in the e-Health domain are identified as follows: (a) healthcare information poses a high level of security and privacy concerns due to its sensitivity; (b) due to vulnerabilities surrounding the healthcare system, a data breach is common and poses a risk for attack by an adversary; and (c) the current privacy and security apparatus needs further fortification. Secondly, Blockchain Technology (BT) is one of the approaches to address these privacy and security issues. The alternative solution is the integration of Differential Privacy (DP) with Blockchain Technology. Thirdly, collections of scientific journals and research papers, published between 2015 and 2022, from IEEE, Science Direct, Google Scholar, ACM, and PubMed on the e-Health domain approach are summarized in terms of security and privacy. The methodology uses a systematic mapping study (SMS) to identify and select relevant research papers and academic journals regarding DP and BT. With this understanding of the current privacy issues in EMR, this paper focuses on three categories: (a) e-Health Record Privacy, (b) Real-Time Health Data, and (c) Health Survey Data Protection. In this study, evidence exists to identify inherent issues and technical challenges associated with the integration of Differential Privacy and Blockchain Technology

Individual Privacy vs Population Privacy: Learning to Attack Anonymization

Over the last decade there have been great strides made in developing techniques to compute functions privately. In particular, Differential Privacy gives strong promises about conclusions that can be drawn about an individual. In contrast, various syntactic methods for providing privacy (criteria such as kanonymity and l-diversity) have been criticized for still allowing private information of an individual to be inferred. In this report, we consider the ability of an attacker to use data meeting privacy definitions to build an accurate classifier. We demonstrate that even under Differential Privacy, such classifiers can be used to accurately infer "private" attributes in realistic data. We compare this to similar approaches for inferencebased attacks on other forms of anonymized data. We place these attacks on the same scale, and observe that the accuracy of inference of private attributes for Differentially Private data and l-diverse data can be quite similar

A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management

It is challenging to balance the privacy and accuracy for federated query processing over multiple private data silos. In this work, we will demonstrate an end-to-end workflow for automating an emerging privacy-preserving technique that uses a deep learning model trained using the Differentially-Private Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual data to answer a query. Our proposed novel declarative privacy-preserving workflow allows users to specify "what private information to protect" rather than "how to protect". Under the hood, the system automatically chooses query-model transformation plans as well as hyper-parameters. At the same time, the proposed workflow also allows human experts to review and tune the selected privacy-preserving mechanism for audit/compliance, and optimization purposes