相关论文: Fuzzy Private Matching (Extended Abstract)

Two party differential privacy allows two parties who do not trust each other, to come together and perform a joint analysis on their data whilst maintaining individual-level privacy. We show that any efficient, computationally…

A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' information to carry out inference. Further, the complete set of…

Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both.…

This study presents the first semi-quantum private comparison protocol under an almost-dishonest third party. The proposed protocol allows two classical participants to compare their secret information without compromising it's privacy. The…

We present new two-party protocols for the Unbalanced Private Set Union (UPSU) problem. Here, the Sender holds a set of data points, and the Receiver holds another (possibly much larger) set, and they would like for the Receiver to learn…

Programmers regularly use strings to encode many types of data, such as Unix file paths, URLs, and email addresses, that are conceptually different. However, existing mainstream programming languages use a unified string type to represent…

Passive operating system fingerprinting reveals valuable information to the defenders of heterogeneous private networks; at the same time, attackers can use fingerprinting to reconnoiter networks, so defenders need obfuscation techniques to…

By introducing a semi-honest third party (TP), we propose in this paper a novel QPC protocol using (n+1)- qubit (n \ge 2) Greenberger-Horne-Zeilinger (GHZ) states as information carriers. The parameter n not only determines the number of…

In this paper, by using d-level single-particle states, two novel multi-party quantum private comparison protocols for size relation comparison with two semi-honest third parties and one semi-honest third party are constructed,…

Biometrics make human identification possible with a sample of a biometric trait and an associated database. Classical identification techniques lead to privacy concerns. This paper introduces a new method to identify someone using his…

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms. In the multi-party feature engineering…

The shuffle model of differential privacy was proposed as a viable model for performing distributed differentially private computations. Informally, the model consists of an untrusted analyzer that receives messages sent by participating…

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where…

Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients. The resulting model is then distributed back to all clients,…

New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(logN) communication complexity of known protocols for the same task, but achieve several significant improvements in security,…

This paper introduces the problem of Private Linear Transformation (PLT) which generalizes the problems of private information retrieval and private linear computation. The PLT problem includes one or more remote server(s) storing…

We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that users in the same cluster share similar model weights. The…

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from…

Fine-tuning large language models (LLMs) raises privacy concerns due to the risk of exposing sensitive training data. Federated learning (FL) mitigates this risk by keeping training samples on local devices, while facing the following…

Emails today are often encrypted, but only between mail servers---the vast majority of emails are exposed in plaintext to the mail servers that handle them. While better than no encryption, this arrangement leaves open the possibility of…