相关论文: Constructing Independently Verifiable Privacy-Comp…
In systems design, we generally distinguish the architecture and the protocol levels. In the context of privacy by design, in the first case, we talk about privacy architectures, which define the privacy goals and the main features of the…
Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is…
Internet of Things (IoT) applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. However, thus far, privacy concerns have not been explicitly considered in software…
Context: Consistent requirements and system specifications are essential for the compliance of software systems towards the General Data Protection Regulation (GDPR). Both artefacts need to be grounded in the original text and conjointly…
This work aims to show the applicability, and how, of privacy by design approach to biometric systems and the benefit of using formal methods to this end. Starting from a general framework that has been introduced at STM in 2014, that…
Existing work on privacy by design mostly focus on technologies rather than methodologies and on components rather than architectures. In this paper, we advocate the idea that privacy by design should also be addressed at the architectural…
Governments around the world are trying to build large data registries for effective delivery of a variety of public services. However, these efforts are often undermined due to serious concerns over privacy risks associated with collection…
Privacy by design will become a legal obligation in the European Community if the Data Protection Regulation eventually gets adopted. However, taking into account privacy requirements in the design of a system is a challenging task. We…
Pervasive use of software applications continues to challenge user privacy when users interact with software systems. Even though privacy practices such as Privacy by Design (PbD), have clear in- structions for software developers to embed…
Implementing privacy by design (PbD) according to the General Data Protection Regulation (GDPR) is met with a growing number of requirements engineering (RE) approaches. However, the question of which RE method for PbD fits best the goals…
Software applications continue to challenge user privacy when users interact with them. Privacy practices (e.g. Data Minimisation (DM), Privacy by Design (PbD) or General Data Protection Regulation (GDPR)) and related "privacy engineering"…
Local differential privacy represents the gold standard for preserving the privacy of data before it leaves the device, and distribution estimation under this model has been well studied. Recently, protocols built upon balanced incomplete…
The complexity of modern integrated circuits (ICs) necessitates collaboration between multiple distrusting parties, including thirdparty intellectual property (3PIP) vendors, design houses, CAD/EDA tool vendors, and foundries, which…
In an ideal network, every packet would be attributable to its sender, while host identities and transmitted content would remain private. Designing such a network is challenging because source accountability and communication privacy are…
Internet of Things (IoT) applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data…
Organizations started to adopt differential privacy (DP) techniques hoping to persuade more users to share personal data with them. However, many users do not understand DP techniques, thus may not be willing to share. Previous research…
A novel private communication framework is proposed where privacy is induced by transmitting over a channel instances of linear inverse problems that are identifiable to the legitimate receiver but unidentifiable to an eavesdropper. The gap…
Children are increasingly using technologies powered by Artificial Intelligence (AI). However, there are growing concerns about privacy risks, particularly for children. Although existing privacy regulations require companies and…
Privacy is an individual choice to determine which personal details can be collected, used and shared. Individual consent and transparency are the core tenets for earning customers trust and this motivates the organizations to adopt privacy…
We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees? In particular, we show that any privacy guarantee that can be efficiently…