Toward an Attribute-Based Digital Identity Modeling for Privacy Preservation

基于属性的授权中的匿名性保障

Guaranteeing Anonymity in Attribute-Based Authorization

针对基于属性的授权方法因属性分布导致身份易被猜测的问题，提出用匿名化数组保障匿名性，并定义局部与全局同质性指标以比较同参数匿名化数组。

A Model for Privacy-enhanced Federated Identity Management

Identity federations operating in a business or consumer context need to prevent the collection of user data across trust service providers for legal and business case reasons. Legal reasons are given by data protection legislation. Other reasons include business owners becoming increasingly aware of confidentiality risks that go beyond traditional information security, e.g., the numbers of authentications to an EDI service might provide insights into the volume of invoices, from which one could derive insider information. This paper proposes extended technical controls supporting three privac…

AI驱动的交互式学习环境眼动追踪中的隐私保护与身份追溯预防

Privacy Preservation and Identity Tracing Prevention in AI-Driven Eye Tracking for Interactive Learning Environments

本文提出一个以人为中心的两阶段隐私保护框架，在保留AI眼动追踪教学益处的同时，通过数据匿名化、联邦学习和安全身份管理，有效防止学生身份回溯，实现高精度诊断分类。

A Novel Microdata Privacy Disclosure Risk Measure

A tremendous amount of individual-level data is generated each day, of use to marketing, decision makers, and machine learning applications. This data often contain private and sensitive information about individuals, which can be disclosed by adversaries. An adversary can recognize the underlying individual's identity for a data record by looking at the values of quasi-identifier attributes, known as identity disclosure, or can uncover sensitive information about an individual through attribute disclosure. In Statistical Disclosure Control, multiple disclosure risk measures have been proposed…

reclaimID: Secure, Self-Sovereign Identities using Name Systems and Attribute-Based Encryption

In this paper we present reclaimID: An architecture that allows users to reclaim their digital identities by securely sharing identity attributes without the need for a centralised service provider. We propose a design where user attributes are stored in and shared over a name system under user-owned namespaces. Attributes are encrypted using attribute-based encryption (ABE), allowing the user to selectively authorize and revoke access of requesting parties to subsets of his attributes. We present an implementation based on the decentralised GNU Name System (GNS) in combination with ciphertext…

Practical Decentralized Attribute-Based Delegation using Secure Name Systems

Identity and trust in the modern Internet are centralized around an oligopoly of identity service providers consisting solely of major tech companies. The problem with centralizing trust has become evident in recent discoveries of mass surveillance and censorship programs as well as information leakage through hacking incidents. One approach to decentralizing trust is distributed, attribute-based access control via attribute-based delegation (ABD). Attribute-based delegation allows a large number of cross-domain attribute issuers to be used in making authorization decisions. Attributes are not…

Task-aware Privacy Preservation for Multi-dimensional Data

Local differential privacy (LDP) can be adopted to anonymize richer user data attributes that will be input to sophisticated machine learning (ML) tasks. However, today's LDP approaches are largely task-agnostic and often lead to severe performance loss -- they simply inject noise to all data attributes according to a given privacy budget, regardless of what features are most relevant for the ultimate task. In this paper, we address how to significantly improve the ultimate task performance with multi-dimensional user data by considering a task-aware privacy preservation problem. The key idea …

IPFed：用于用户身份认证的身份保护联邦学习

IPFed: Identity protected federated learning for user authentication

针对用户认证中隐私保护与高准确率难以兼顾的问题，提出基于随机投影类别嵌入的联邦学习方法 IPFed，并证明其可达到最先进方法的等价性能。

Achieving Data Truthfulness and Privacy Preservation in Data Markets

As a significant business paradigm, many online information platforms have emerged to satisfy society's needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we prop…

Killing the Password and Preserving Privacy with Device-Centric and Attribute-based Authentication

Current authentication methods on the Web have serious weaknesses. First, services heavily rely on the traditional password paradigm, which diminishes the end-users' security and usability. Second, the lack of attribute-based authentication does not allow anonymity-preserving access to services. Third, users have multiple online accounts that often reflect distinct identity aspects. This makes proving combinations of identity attributes hard on the users. In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attribute-based authenticat…

SPAct: Self-supervised Privacy Preservation for Action Recognition

Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames of video dataset for privacy labels is not feasible. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. For the first time, we present a novel training framework which removes privacy information from input video in …

基于个人数据根本理解与演化的威胁景观的隐私风险预测

Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape

本文提出了一种基于身份生态系统图的隐私风险预测框架，通过分析身份盗窃和欺诈案例，利用图神经网络预测个人数据泄露的可能性。