Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

Nowadays,Internet of Things(IoT)is widely deployed and brings great opportunities to change people's daily life.To realize more effective human-computer interaction in the IoT applications,the Question Answering(QA)systems implanted in the IoT services are supposed to improve the ability to understand natural language.Therefore,the distributed representation of words,which contains more semantic or syntactic information,has been playing a more and more important role in the QA systems.However,learning high-quality distributed word vectors requires lots of storage and computing resources,hence it cannot be deployed on the resource-constrained IoT devices.It is a good choice to outsource the data and computation to the cloud servers.Nevertheless,it could cause privacy risks to directly upload private data to the untrusted cloud.Therefore,realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task.In this paper,we present a novel efficient word vector learning scheme over encrypted data.We first design a series of arithmetic computation protocols.Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data.The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy.Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

Research progress on risk factors for leukoaraiosis

Leukoaraiosis (LA), a term of neural imaging, is a disease which clinically causes cognitive dysfunction and gait disorders, eventually leads to persistent or progressive cognitive and neural dysfunction, seriously affects patients' daily lives. Early detection and identification of LA and its risk factors and early intervention may be of help to improve the quality of patients' living in the future. The research progress on risk factors for LA was reviewed in this study.

A Scheduling Optimization Technique Based on Reuse in Spark to Defend Against APT Attack

Advanced Persistent Threat （APT） attack, an attack option in recent years, poses serious threats to the security of governments and enterprises data due to its advanced and persistent attacking characteristics. To address this issue, a security policy of big data analysis has been proposed based on the analysis of log data of servers and terminals in Spark. However, in practical applications, Spark cannot suitably analyze very huge amounts of log data. To address this problem, we propose a scheduling optimization technique based on the reuse of datasets to improve Spark performance. In this technique, we define and formulate the reuse degree of Directed Acyclic Graphs （DAGs） in Spark based on Resilient Distributed Datasets （RDDs）. Then, we define a global optimization function to obtain the optimal DAG sequence, that is, the sequence with the least execution time. To implement the global optimization function, we further propose a novel cost optimization algorithm based on the traditional Genetic Algorithm （GA）. Our experiments demonstrate that this scheduling optimization technique in Spark can greatly decrease the time overhead of analyzing log data for detecting APT attacks.

BVDFed:Byzantine-resilient and verifiable aggregation for differentially private federated learning

Federated Learning(FL)has emerged as a powerful technology designed for collaborative training between multiple clients and a server while maintaining data privacy of clients.To enhance the privacy in FL,Differentially Private Federated Learning(DPFL)has gradually become one of the most effective approaches.As DPFL operates in the distributed settings,there exist potential malicious adversaries who manipulate some clients and the aggregation server to produce malicious parameters and disturb the learning model.However,existing aggregation protocols for DPFL concern either the existence of some corrupted clients(Byzantines)or the corrupted server.Such protocols are limited to eliminate the effects of corrupted clients and server when both are in existence simultaneously due to the complicated threat model.In this paper,we elaborate such adversarial threat model and propose BVDFed.To our best knowledge,it is the first Byzantine-resilient and Verifiable aggregation for Differentially privateFEDerated learning.In specific,wepropose Differentially Private Federated Averaging algorithm(DPFA)asour primary workflow of BVDFed,which ismore lightweight and easily portable than traditional DPFL algorithm.We then introduce Loss Score to indicate the trustworthiness of disguised gradients in DPFL.Based on Loss Score,we propose an aggregation rule DPLoss to eliminate faulty gradients from Byzantine clients during server aggregation while preserving the privacy of clients'data.Additionally,we design a secure verification scheme DPVeri that are compatible with DPFA and DPLoss to support the honest clients in verifying the integrity of received aggregated results.And DPVeri also provides resistance to collusion attacks with no more than t participants for our aggregation.Theoretical analysis and experimental results demonstrate our aggregation to be feasible and effective in practice.