Related papers: On SDVS Sender Privacy In The Multi-Party Setting
Differential privacy (DP) is a mathematical definition of privacy that can be widely applied when publishing data. DP has been recognized as a potential means of adhering to various privacy-related legal requirements. However, it can be…
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We…
Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting scenarios, where voting rules are required to be…
Differential privacy is a widely used notion of security that enables the processing of sensitive information. In short, differentially private algorithms map "neighbouring" inputs to close output distributions. Prior work proposed several…
This paper presents a privacy-preserving protocol for identity registration and information sharing in federated authentication systems. The goal is to enable Identity Providers (IdPs) to detect duplicate or fraudulent identity enrollments…
One of scenarios in data-sharing applications is that files are managed by multiple owners, and the list of file owners may change dynamically. However, most existing solutions to this problem rely on trusted third parties and have…
Differential privacy provides a formal approach to privacy of individuals. Applications of differential privacy in various scenarios, such as protecting users' original utterances, must satisfy certain mathematical properties. Our…
Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as password-protected chats and end-to-end encryption, which primarily protect message contents. Beyond contents, a lot can be inferred about…
In light of the GDPR, data controllers (DC) need to allow data subjects (DS) to exercise certain data subject rights. A key requirement here is that DCs can reliably authenticate a DS. Due to a lack of clear technical specifications, this…
As the use of differential privacy (DP) becomes widespread, the development of effective tools for reasoning about the privacy guarantee becomes increasingly critical. In pursuit of this goal, we demonstrate novel relationships between DP…
This paper discusses a new protocol implementing authentication in a multi-located environment that avoids man-in-the-middle (MIM) attack, replay attack and provides privacy, integrity of a message for multi-located parties. The protocol…
Based on the two-step protocol [Phys. Rev. A68(03)042317], we propose a $(n,n)$-threshold multiparty quantum secret sharing protocol of secure direct communication. In our protocol only all the sharers collaborate can the sender's secure…
Implicit authentication consists of a server authenticating a user based on the user's usage profile, instead of/in addition to relying on something the user explicitly knows (passwords, private keys, etc.). While implicit authentication…
Redactable signature schemes and sanitizable signature schemes are methods that permit modification of a given digital message and retain a valid signature. This can be applied to decentralized identity systems for delegating identity…
Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. Due to its efficiency and flexibility, Genetic Programming…
Secure Multiparty Computation (SMC) allows parties to know the result of cooperative computation while preserving privacy of individual data. Secure sum computation is an important application of SMC. In our proposed protocols parties are…
Quantum digital signatures (QDSs) promise information-theoretic security against repudiation and forgery of messages. Compared with currently existing three-party QDS protocols, multiparty protocols have unique advantages in the practical…
Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained…
Emerging systems such as smart grids or intelligent transportation systems often require end-user applications to continuously send information to external data aggregators performing monitoring or control tasks. This can result in an…
Recent results have shown the usefulness of tamper-proof hardware tokens as a setup assumption for building UC-secure two-party computation protocols, thus providing broad security guarantees and allowing the use of such protocols as…