时间：2022月6月14日 星期二下午14:30

地点：文科楼E103（经济与管理学院）

标题：《A Forward and Backward Private Oblivious RAM for Storage Outsourcing on Edge-Cloud Computing》

汇报人1：蔡竹斌

摘要：

In recent years, edge-cloud computing is regarded as a promising solution to meet the requirements of mobile computing and Internet-of-Things (IoT). However, due to the limited storage resources of edge equipment, there is a security threat when users outsource their sensitive data to the cloud computing center. The users usually adopt a data-encryption approach, Oblivious RAM (ORAM), which enables a user to read/write her outsourced private data without access pattern leakage. Not all users like the fully functional ORAM all the time since the ORAM protocol is usually highly interactive or occupies large edge storage space. We show that forward-private/backward-private (FP/BP) ORAMs are good alternatives for secure storage outsourcing. We introduce the FP/BP-ORAM definitions and present LL-ORAM, the first FP/BP-ORAM that achieves near-zero edge storage, single-roundtrip read/write, and worst-case sublinear access time. For any outsourced record, LL-ORAM provides both an oblivious-access interface and a nonoblivious-access interface. FP-ORAM concerns more data-write privacy than data-read privacy. BP-ORAM concerns more data-read privacy. The constructions involve a tree data structure named LL-tree, which supports fast computation in the cloud with an access-pattern-reduced leakage profile. The security analysis shows that LL-ORAM meets the proposed forward and backward security model. The experimental results demonstrate that LL-ORAM is round-efficient and can be deployed on edge-cloud computing systems.

录取期刊：Journal of Parallel and Distributed Computing

标题：《A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption》

汇报人2：孔新新

摘要：

This paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new 6D fractional-order memristive Hopfield neural network (6D-FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcation characteristics and coexistence attractor characteristics of fractional memristor Hopfield neural network is studied. Because this 6D-FMHNN has chaotic characteristics, we also use this 6D-FMHNN to generate a random number and apply it to the field of image encryption. We make a series of analysis on the randomness of random numbers and the security of image encryption, and prove that the encryption algorithm using this 6D-FMHNN is safe and sensitive to the key.

录取期刊：Frontiers in Physics

标题：《Dynamic analysis and application in medical digital image watermarking of a new multi-scroll neural network with quartic nonlinear memristor》

汇报人3：陈会锋

摘要：

Memristor is widely used in various neural bionic models because of its excellent characteristics in biological neural activity simulation. In this paper, a piecewise nonlinear function is used to transform the quartic memristor, which is introduced into the ternary Hopfield neural network (HNN) with self-feedback, and a piecewise quartic memristive chaotic neural network model with multi-scroll is constructed. Through simulation analysis, the number of scroll layers changes with memristor parameters and has significant coexistence of multi-scroll attractors and high initial value sensitivity has been found. Using its excellent unpredictability, a digital watermarking algorithm based on wavelet transform is improved and used in the protection of personal medical data. The results show that it not only improves the confidentiality and convenience, but also ensures its robustness and has good encryption effect.

录取期刊：The European Physical Journal Plus