Gergely Biczók
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View article: Modeling interdependent privacy threats
Modeling interdependent privacy threats Open
The rise of online social networks, user-gene-rated content, and third-party apps made data sharing an inevitable trend, driven by both user behavior and the commercial value of personal information. As service providers amass vast amounts…
View article: Anonymity-Washing
Anonymity-Washing Open
View article: Incentivizing Secure Software Development: The Role of Voluntary Audit and Liability Waiver
Incentivizing Secure Software Development: The Role of Voluntary Audit and Liability Waiver Open
Misaligned incentives in secure software development have long been a challenge in security economics. Product liability, a powerful legal framework in other industries, has been largely ineffective for software products until recent times…
View article: Modeling interdependent privacy threats
Modeling interdependent privacy threats Open
The rise of online social networks, user-gene-rated content, and third-party apps made data sharing an inevitable trend, driven by both user behavior and the commercial value of personal information. As service providers amass vast amounts…
View article: The Cyber Alliance Game: How Alliances Influence Cyber-Warfare
The Cyber Alliance Game: How Alliances Influence Cyber-Warfare Open
Cyber-warfare has become the norm in current ongoing military conflicts. Over the past decade, numerous examples have shown the extent to which nation-states become vulnerable if they do not focus on building their cyber capacities. Adding…
View article: Effective Anonymous Messaging: the Role of Altruism
Effective Anonymous Messaging: the Role of Altruism Open
Anonymous messaging and payments have gained momentum recently due to their impact on individuals, society, and the digital landscape. Fuzzy Message Detection (FMD) is a privacy-preserving protocol where an untrusted server performs messag…
View article: Incremental federated learning for traffic flow classification in heterogeneous data scenarios
Incremental federated learning for traffic flow classification in heterogeneous data scenarios Open
This paper explores the comparative analysis of federated learning (FL) and centralized learning (CL) models in the context of multi-class traffic flow classification for network applications, a timely study in the context of increasing pr…
View article: IDPFilter: Mitigating Interdependent Privacy Issues in Third-Party Apps
IDPFilter: Mitigating Interdependent Privacy Issues in Third-Party Apps Open
Third-party applications have become an essential part of today's online ecosystem, enhancing the functionality of popular platforms. However, the intensive data exchange underlying their proliferation has increased concerns about interdep…
View article: Incentivizing Secure Software Development: the Role of Voluntary Audit and Liability Waiver
Incentivizing Secure Software Development: the Role of Voluntary Audit and Liability Waiver Open
Misaligned incentives in secure software development have long been the focus of research in the economics of security. Product liability, a powerful legal framework in other industries, has been largely ineffective for software products u…
View article: Quality Inference in Federated Learning With Secure Aggregation
Quality Inference in Federated Learning With Secure Aggregation Open
Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mech…
View article: 6G for Connected Sky: A Vision for Integrating Terrestrial and Non-Terrestrial Networks
6G for Connected Sky: A Vision for Integrating Terrestrial and Non-Terrestrial Networks Open
In this paper, we present the vision of our project 6G for Connected Sky (6G-SKY) to integrate terrestrial networks (TNs) and non-terrestrial networks (NTNs) and outline the current research activities in 6G research projects in comparison…
View article: SECREDAS: Safe and (Cyber-)Secure Cooperative and Automated Mobility
SECREDAS: Safe and (Cyber-)Secure Cooperative and Automated Mobility Open
Infrastructure-to-Vehicle (I2V) and Vehicle-to-Infrastructure (V2I) communication is likely to be a key-enabling technology for automated driving in the future. Using externally placed sensors, the digital infrastructure can support the ve…
View article: SECREDAS: Safe and (Cyber-) Secure Cooperative and Automated Mobility
SECREDAS: Safe and (Cyber-) Secure Cooperative and Automated Mobility Open
Infrastructure-to-Vehicle (I2V) and Vehicle-to-Infrastructure (V2I) communication is likely to be a key-enabling technology for automated driving in the future. Using externally placed sensors, the digital infrastructure can support the ve…
View article: Privacy pitfalls of releasing in-vehicle network data
Privacy pitfalls of releasing in-vehicle network data Open
The ever-increasing volume of vehicular data has enabled different service providers to access and monetize in-vehicle network data of millions of drivers. However, such data often carry personal or even potentially sensitive information, …
View article: In Search of Lost Utility: Private Location Data
In Search of Lost Utility: Private Location Data Open
The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location data since previous techniques all suffer from the inherent sp…
View article: Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response
Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response Open
Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing, contact tracing, and vaccination. However, combining these measures into an efficient holistic pandemic r…
View article: Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic\n Response
Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic\n Response Open
Most governments employ a set of quasi-standard measures to fight COVID-19\nincluding wearing masks, social distancing, virus testing, contact tracing, and\nvaccination. However, combining these measures into an efficient holistic\npandemi…
View article: Detecting Message Modification Attacks on the CAN Bus with Temporal Convolutional Networks
Detecting Message Modification Attacks on the CAN Bus with Temporal Convolutional Networks Open
Multiple attacks have shown that in-vehicle networks have vulnerabilities\nwhich can be exploited. Securing the Controller Area Network (CAN) for modern\nvehicles has become a necessary task for car manufacturers. Some attacks inject\npote…
View article: In Search of Lost Utility: Private Location Data
In Search of Lost Utility: Private Location Data Open
The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location data since previous techniques all suffer from the inherent sp…
View article: Privacy-preserving release of mobility data: a clean-slate approach.
Privacy-preserving release of mobility data: a clean-slate approach. Open
The quantity of mobility data is overwhelming nowadays providing tremendous potential for various value-added services. While the benefits of these mobility datasets are apparent, they also provide significant threat to location privacy. A…
View article: Quality Inference in Federated Learning with Secure Aggregation
Quality Inference in Federated Learning with Secure Aggregation Open
Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mech…
View article: Corona Games: Masks, Social Distancing and Mechanism Design
Corona Games: Masks, Social Distancing and Mechanism Design Open
Pandemic response is a complex affair. Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing and contact tracing. We argue that some non-trivial factors behind …
View article: Automatic Driver Identification from In-Vehicle Network Logs
Automatic Driver Identification from In-Vehicle Network Logs Open
Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the measurements of the mul…
View article: Table of contents
Table of contents Open
View article: Towards protected VNFs for multi-operator service delivery
Towards protected VNFs for multi-operator service delivery Open
Value-added 5G verticals are foreseen to be delivered as a service chain over multiple network operators with extensive outsourcing of Virtual Network Functions (VNFs). In this short paper we introduce the initial design of SafeLib, a soft…
View article: Together or Alone: The Price of Privacy in Collaborative Learning
Together or Alone: The Price of Privacy in Collaborative Learning Open
Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium siz…
View article: Extracting vehicle sensor signals from CAN logs for driver re-identification
Extracting vehicle sensor signals from CAN logs for driver re-identification Open
Data is the new oil for the car industry. Cars generate data about how they are used and who's behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of…
View article: Extracting Vehicle Sensor Signals from CAN Logs for Driver Re-identification
Extracting Vehicle Sensor Signals from CAN Logs for Driver Re-identification Open
Data is the new oil for the car industry. Cars generate data about how they are used and who’s behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of…
View article: Collateral damage of Facebook third-party applications: a comprehensive study
Collateral damage of Facebook third-party applications: a comprehensive study Open
View article: Together or Alone: The Price of Privacy in Collaborative Learning
Together or Alone: The Price of Privacy in Collaborative Learning Open
Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium siz…