Federated learning is a distributed machine learning technique that trains a global model by exchanging model parameters or intermediate results among multiple data sources. Although federated learning achieves physic...Federated learning is a distributed machine learning technique that trains a global model by exchanging model parameters or intermediate results among multiple data sources. Although federated learning achieves physical isolation of data, the local data of federated learning clients are still at risk of leakage under the attack of malicious individuals. For this reason, combining data protection techniques (e.g., differential privacy techniques) with federated learning is a sure way to further improve the data security of federated learning models. In this survey, we review recent advances in the research of differentially-private federated learning models. First, we introduce the workflow of federated learning and the theoretical basis of differential privacy. Then, we review three differentially-private federated learning paradigms: central differential privacy, local differential privacy, and distributed differential privacy. After this, we review the algorithmic optimization and communication cost optimization of federated learning models with differential privacy. Finally, we review the applications of federated learning models with differential privacy in various domains. By systematically summarizing the existing research, we propose future research opportunities.展开更多
The main purpose of this paper is to introduce the LWE public key cryptosystem with its security. In the first section, we introduce the LWE public key cryptosystem by Regev with its applications and some previous res...The main purpose of this paper is to introduce the LWE public key cryptosystem with its security. In the first section, we introduce the LWE public key cryptosystem by Regev with its applications and some previous research results. Then we prove the security of LWE public key cryptosystem by Regev in detail. For not only independent identical Gaussian disturbances but also any general independent identical disturbances, we give a more accurate estimation probability of decryption error of general LWE cryptosystem. This guarantees high security and widespread applications of the LWE public key cryptosystem.展开更多
In this article, we introduce the discrete subgroup in ℝ<sup>n</sup> as preliminaries first. Then we provide some theories of cyclic lattices and ideal lattices. By regarding the cyclic lattices...In this article, we introduce the discrete subgroup in ℝ<sup>n</sup> as preliminaries first. Then we provide some theories of cyclic lattices and ideal lattices. By regarding the cyclic lattices and ideal lattices as the correspondences of finitely generated R-modules, we prove our main theorem, i.e. the correspondence between cyclic lattices in ℝ<sup>n</sup> and finitely generated R-modules is one-to-one. Finally, we give an explicit and countable upper bound for the smoothing parameter of cyclic lattices.展开更多
文摘Federated learning is a distributed machine learning technique that trains a global model by exchanging model parameters or intermediate results among multiple data sources. Although federated learning achieves physical isolation of data, the local data of federated learning clients are still at risk of leakage under the attack of malicious individuals. For this reason, combining data protection techniques (e.g., differential privacy techniques) with federated learning is a sure way to further improve the data security of federated learning models. In this survey, we review recent advances in the research of differentially-private federated learning models. First, we introduce the workflow of federated learning and the theoretical basis of differential privacy. Then, we review three differentially-private federated learning paradigms: central differential privacy, local differential privacy, and distributed differential privacy. After this, we review the algorithmic optimization and communication cost optimization of federated learning models with differential privacy. Finally, we review the applications of federated learning models with differential privacy in various domains. By systematically summarizing the existing research, we propose future research opportunities.
文摘The main purpose of this paper is to introduce the LWE public key cryptosystem with its security. In the first section, we introduce the LWE public key cryptosystem by Regev with its applications and some previous research results. Then we prove the security of LWE public key cryptosystem by Regev in detail. For not only independent identical Gaussian disturbances but also any general independent identical disturbances, we give a more accurate estimation probability of decryption error of general LWE cryptosystem. This guarantees high security and widespread applications of the LWE public key cryptosystem.
文摘In this article, we introduce the discrete subgroup in ℝ<sup>n</sup> as preliminaries first. Then we provide some theories of cyclic lattices and ideal lattices. By regarding the cyclic lattices and ideal lattices as the correspondences of finitely generated R-modules, we prove our main theorem, i.e. the correspondence between cyclic lattices in ℝ<sup>n</sup> and finitely generated R-modules is one-to-one. Finally, we give an explicit and countable upper bound for the smoothing parameter of cyclic lattices.
