EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

The Bacteria in a Giant's Body--Analysis of Problems in USA Politics

The United States is the first bourgeois republic nation.The history of its political system dates back to 400 years ago.In the history of the American Continent,'The May Flower Treaty' for the first time wrote the democratic thoughts on the paper.And the Virginia Congress was the earliest found congress,which made the Congress a basic political system in the colonies.The Independent Declaration passed on July 4th,1776 established the basic system in the colonies.In 1787 when 'The Constitution of United States' was made and passed,the political system centered on 'checks and balances' was finally confirmed.Since that time,the American politicians have been boasting their most 'advanced' and 'perfect' political system all over the world and trying to promote it all over the world.In fact,more than half of the countries in the world really applied the American political system in their bourgeois revolution.However,the financial crisis originated in USA in 2008 strongly questioned the effectiveness of American political system.The USA government's performance in the crisis was widely criticized by other countries.This dissertation will analyze the problems exposed in the execution of the American political system centered on 'checks and balances'.The writer will exam the problems shown in this time's financial crisis and pay attention to the problems of the two-party system,the electoral system,etc.The writer hopes that the dissertation can give some lights and create a new view.

A provable-secure and practical two-party distributed signing protocol for SM2 signature algorithm

Mobile devices are widely used for data access,communications and storage.However,storing a private key for signature and other cryptographic usage on a single mobile device can be challenging,due to its computational limitations.Thus,a number of(t,n)threshold secret sharing schemes designed to minimize private key from leakage have been proposed in the literature.However,existing schemes generally suffer from key reconstruction attack.In this paper,we propose an efficient and secure two-party distributed signing protocol for the SM2 signature algorithm.The latter has been mandated by the Chinese government for all electronic commerce applications.The proposed protocol separates the private key to storage on two devices and can generate a valid signature without the need to reconstruct the entire private key.We prove that our protocol is secure under nonstandard assumption.Then,we implement our protocol using MIRACL Cryptographic SDK to demonstrate that the protocol can be deployed in practice to prevent key disclosure.

Secure Two-Party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks

Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks （WSN）, secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side＇s secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol （PPSPP） for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol （HEEDP） without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.

Two-party certificateless authenticated key agreement protocol with enhanced security

Two-party certificateless authenticated key agreement(CL-AKA) protocol is a hot topic in the field of wireless communication security. An improved two-party CL-AKA protocol with enhanced security is proposed,which is of provable security and unforgeability in the extended Canetti-Krawczyk(eCK) security model based on the hardness assumption of the computational Diffie Hellman(CDH) problem. Compared with other similar protocols, it is more efficient and can satisfy security properties such as free of the centralized management of certificate and key, free of bilinear pairings, two-party authentication, resistant to unknown key-share attack, key compromise impersonation attacks, the man-in-the-middle-attack(MIMA) of key generation center(KGC), etc. These properties make the proposed protocol have better performance and adaptability for military communication.

Triggered query with strong location privacy in mobile network

Location privacy is a hot-button topic that has to be taken into account if location-based services （LBS） are to succeed. Extensive researches focus on the nearest neighbor （NN） query or k-nearest neighbor （kNN） query about location privacy-preserving. However, no single technique can be applied to any situation and achieve high security and low cost. This manuscript focuses on the location privacy-preserving in the geo-fencing services, A secure two-party computation location privacy model and the corresponding solution was proposes based on triggered query. The author draw on the computational geometry and cryptography technologies, mainly to conquer such problems related to the users＇ location hidden, secret checking-in and secret authentication in the geo-fencing services. Performance assessment shows that the proposed solution can reduce the query-processing time and the size of query result set.