Related papers: Privacy-Enhancing Encryption in Data Sharing: A Su…
Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of next-generation sequencing data and in the design of…
Attribute-based encryption (ABE) is a promising cryptographic mechanism for providing confidentiality and fine-grained access control in the cloud-based area. However, due to high computational overhead, common ABE schemes are not suitable…
Harmful repercussions from sharing sensitive or personal data can hamper institutions' willingness to engage in data exchange. Thus, institutions consider Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs)…
As data-driven and AI-based decision making gains widespread adoption across disciplines, it is crucial that both data privacy and decision fairness are appropriately addressed. Although differential privacy (DP) provides a robust framework…
Attribute-based encryption is a form of encryption which offers the capacity to encrypt data such that it is only accessible to individuals holding a satisfactory configuration of attributes. As cloud and distributed computing become more…
Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is…
Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, potentials of the cloud services have not been fully unleashed due to users' concerns…
Personalized AI agents rely on access to a user's digital footprint, which often includes sensitive data from private emails, chats and purchase histories. Yet this access creates a fundamental societal and privacy risk: systems lacking…
Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main enabler of economic growth and shared prosperity in modern-day societies. However, the complex exchange of goods, services,…
Privacy has always been a critical issue in the digital era, particularly with the increasing use of Internet of Things (IoT) devices. As the IoT continues to transform industries such as healthcare, smart cities, and home automation, it…
As data collection and analysis become critical functions for many cloud applications, proper data sharing with approved parties is required. However, the traditional data sharing scheme through centralized data escrow servers may sacrifice…
Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the…
Privacy and data protection constitute core values of individuals and of democratic societies. There have been decades of debate on how those values -and legal obligations- can be embedded into systems, preferably from the very beginning of…
The rapid growth in digital data forms the basis for a wide range of new services and research, e.g, large-scale medical studies. At the same time, increasingly restrictive privacy concerns and laws are leading to significant overhead in…
Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to preserve privacy whilst also obtaining the benefits of…
With the ever-growing concern for internet security, the field of quantum cryptography emerges as a promising solution for enhancing the security of networking systems. In this paper, 20 notable papers from leading conferences and journals…
Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for…
The use of Neural Networks (NNs) for sensitive data processing is becoming increasingly popular, raising concerns about data privacy and security. Homomorphic Encryption (HE) has the potential to be used as a solution to preserve data…
Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a)…
Multi-access Edge Computing (MEC), an enhancement of 5G, processes data closer to its generation point, reducing latency and network load. However, the distributed and edge-based nature of 5G-MEC presents privacy and security challenges,…