相关论文: Glass-Vault: A Generic Transparent Privacy-preserv…
Differential privacy (DP) has become the gold standard for preserving individual privacy in data analysis. However, an implicit yet fundamental assumption underlying these rigorous privacy guarantees is the correct implementation and…
Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as…
Transparency and accountability are indispensable principles for modern data protection, from both, legal and technical viewpoints. Regulations such as the GDPR, therefore, require specific transparency information to be provided including,…
This paper proposes a novel visual model for web applications security monitoring. Although an automated intrusion detection system can shield a web application from common attacks, it usually cannot detect more complicated break-ins. So, a…
Despite increasing advancements in today's information exchange infrastructure, the preservation of user data and privacy still remains a problem. Both insecure baselines and secure solutions leak user data. For example, Certificate…
Analytics on video recorded by cameras in public areas have the potential to fuel many exciting applications, but also pose the risk of intruding on individuals' privacy. Unfortunately, existing solutions fail to practically resolve this…
The emergence of various technologies has since pushed researchers to develop new protocols that support high density data transmissions in Wide Area Networks. Many of these protocols are TCP protocol variants, which have demonstrated…
Data-driven applications and services have been increasingly deployed in all aspects of life including healthcare and medical services in which a huge amount of personal data is collected, aggregated, and processed in a centralised server…
This paper presents Droplet, a decentralized data access control service. Droplet enables data owners to securely and selectively share their encrypted data while guaranteeing data confidentiality in the presence of unauthorized parties and…
Learning on graphs is becoming prevalent in a wide range of applications including social networks, robotics, communication, medicine, etc. These datasets belonging to entities often contain critical private information. The utilization of…
Contact tracing has grown in popularity as a promising solution to the COVID-19 pandemic. The benefits of automated contact tracing are two-fold. Contact tracing promises to reduce the number of infections by being able to: 1)…
Digital contact tracing has been proposed to support the health authorities in fighting the current Covid-19 pandemic. In this paper we propose two centralised protocols for digital contact tracing that, contrary to the common hypothesis…
Deep learning (DL) is being increasingly utilized in healthcare-related fields due to its outstanding efficiency. However, we have to keep the individual health data used by DL models private and secure. Protecting data and preserving the…
As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that…
Cyber Threat Intelligence (CTI) sharing is an important activity to reduce information asymmetries between attackers and defenders. However, this activity presents challenges due to the tension between data sharing and confidentiality, that…
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client data aggregation. The framework enforces unanimous-release confidentiality…
In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's location data to eliminate the outbreak. However, it raises…
Privacy concerns in healthcare have gained interest recently via GDPR, with a rising need for privacy-preserving data collection methods that keep personal information hidden in otherwise usable data. Sometimes data needs to be encrypted…
Since SARS-CoV-2 started spreading in Europe in early 2020, there has been a strong call for technical solutions to combat or contain the pandemic, with contact tracing apps at the heart of the debates. The EU's General Daten Protection…
Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in the implementation and communication of DP. This paper presents…