相关论文: PSI ({\Psi}): a Private data Sharing Interface

The explosion of data collection has raised serious privacy concerns in users due to the possibility that sharing data may also reveal sensitive information. The main goal of a privacy-preserving mechanism is to prevent a malicious third…

Sensor-based interactive systems -- e.g., "smart" speakers, webcams, and RFID tags -- allow us to embed computational functionality into physical environments. They also expose users to real and perceived privacy risks: users know that…

In the current global situation-burdened by, among others, a vast number of people without formal identification, digital leap, the need for health passports and contact tracking applications-providing private and secure digital identity…

In response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques such as differential privacy (DP) that inject statistical noise when generating published datasets. These…

A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The…

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions…

The intensive flow of personal data associated with the trend of computerizing aspects of people's diversity in their daily lives is associated with issues concerning not only people protection and their trust in new technologies, but also…

Private handshaking allows pairs of users to determine which (secret) groups they are both a member of. Group membership is kept secret to everybody else. Private handshaking is a more private form of secret handshaking, because it does not…

Enforcement of privacy regulation is essential for collaborative data analytics. In this work, we address a scenario in which two companies expect to securely join their datasets with respect to their common customers to maximize data…

People who are marginalized experience disproportionate harms when their privacy is violated. Meeting their needs is vital for developing equitable and privacy-protective technologies. In response, research at the intersection of privacy…

We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in order to protect individuals' privacy. Synthetic data is a…

Organisations disclose their privacy practices by posting privacy policies on their website. Even though users often care about their digital privacy, they often don't read privacy policies since they require a significant investment in…

Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially private, adding noise to each aggregation is not enough: we…

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this…

Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally…

OpenData movement around the globe is demanding more access to information which lies locked in public or private servers. As recently reported by a McKinsey publication, this data has significant economic value, yet its release has…

This article presents the potential use of the Self-Sovereign Identities (SSI), combining with Distributed Ledger Technologies (DLT), to improve the privacy and control of health data. The paper presents the SSI technology, lists the…

The use of AI tools in research is becoming routine, alongside growing consensus that such use should be transparently disclosed. However, AI disclosure statements remain rare and inconsistent, with policies offering limited guidance and…

Differential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the provision of objective privacy guarantees. However, while…

Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. It makes no assumptions about the knowledge or computational power of adversaries, and…