The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Silver nanoparticles (AgNPs) can be synthesized from a variety of techniques including physical, chemical and biological routes. They have been widely used as nanomaterials for manufacturing cosmetic and healthcare products, antimicrobial textiles, wound dressings, antitumor drug carriers, etc. due to their excellent antimicrobial properties. Accordingly, AgNPs have gained access into our daily life, and the inevitable human exposure to these nanoparticles has raised concerns about their potential hazards to the environment, health, and safety in recent years. From in vitro cell cultivation tests, AgNPs have been reported to be toxic to several human cell lines including human bronchial epithelial cells, human umbilical vein endothelial cells, red blood cells, human peripheral blood mononuclear cells, immortal human keratinocytes, liver cells, etc. AgNPs induce a dose-, size- and time-dependent cytotoxicity, particularly for those with sizes ≤10 nm. Furthermore, AgNPs can cross the brain blood barrier of mice through the circulation system on the basis of in vivo animal tests. AgNPs tend to accumulate in mice organs such as liver, spleen, kidney and brain following intravenous, intraperitoneal, and intratracheal routes of administration. In this respect, AgNPs are considered a double-edged sword that can eliminate microorganisms but induce cytotoxicity in mammalian cells. This article provides a state-of-the-art review on the synthesis of AgNPs, and their applications in antimicrobial textile fabrics, food packaging films, and wound dressings. Particular attention is paid to the bactericidal activity and cytotoxic effect in mammalian cells.

Bactericidal and Cytotoxic Properties of Silver Nanoparticles.

Solid composite electrolytes (SCEs) that combine the advantages of solid polymer electrolytes (SPEs) and inorganic ceramic electrolytes (ICEs) present acceptable ionic conductivity, high mechanical strength, and favorable interfacial contact with electrodes, which greatly improve the electrochemical performance of all-solid-state batteries compared to single SPEs and ICEs. However, there are many challenges to overcome before the practical application of SCEs, including the low ionic conductivity less than 10-3 S cm-1 at ambient temperature, poor interfacial stability, and high interfacial resistance, which greatly restrict the room temperature performance. Herein, the advances of SCEs applied in all-solid-state lithium batteries are presented, including the Li ion migration mechanism of SCEs, the strategies to enhance the ionic conductivity of SCEs by various morphologies of ICEs, and construction methods of the low resistance and stable interfaces of SCEs with both cathode and anode. Finally, some typical applications of SCEs in lithium batteries are summarized and future development directions are prospected. This work presents how it is quite significant to further enhance the ionic conductivity of SCEs by developing the novel SPEs with the special morphology of ICEs for advanced all-solid-state lithium batteries.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.201903088

Progress and Perspective of Ceramic/Polymer Composite Solid Electrolytes for Lithium Batteries

Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.

Edge Computing for Autonomous Driving: Opportunities and Challenges

Hydrogen atom makes graphene magnetic Graphene has many extraordinary mechanical and electronic properties, but it's not magnetic. To make it so, the simplest strategy is to modify its electronic structure to create unpaired electrons. Researchers can do that by, for example, removing individual carbon atoms or adsorbing hydrogen onto graphene. This has to be done in a very controlled way because of a peculiarity of the graphene's crystal lattice, which consists of two sublattices. Gonzales-Herrero et al. deposited a single hydrogen atom on top of graphene and used scanning tunneling microscopy to detect magnetism on the sublattice lacking the deposited atom (see the Perspective by Hollen and Gupta). Science, this issue p. 437; see also p. 415 Scanning tunneling microscopy shows that a hydrogen atom deposited on graphene makes the complementary sublattice magnetic. [Also see Perspective by Hollen and Gupta] Isolated hydrogen atoms absorbed on graphene are predicted to induce magnetic moments. Here we demonstrate that the adsorption of a single hydrogen atom on graphene induces a magnetic moment characterized by a ~20–millielectron volt spin-split state at the Fermi energy. Our scanning tunneling microscopy (STM) experiments, complemented by first-principles calculations, show that such a spin-polarized state is essentially localized on the carbon sublattice opposite to the one where the hydrogen atom is chemisorbed. This atomically modulated spin texture, which extends several nanometers away from the hydrogen atom, drives the direct coupling between the magnetic moments at unusually long distances. By using the STM tip to manipulate hydrogen atoms with atomic precision, it is possible to tailor the magnetism of selected graphene regions.

/pdf/atomic-scale-control-of-graphene-magnetism-by-using-hydrogen-53d9rq6gri.pdf

Atomic-scale control of graphene magnetism by using hydrogen atoms

The invention discloses a differential privacy protecting method of datastream key mode mining. A window corresponding to each timestamp is preprocessed and mined, firstly first noise adding is conducted, so that counting (a frequent mode) satisfies differential privacy, and then through a branch number distribution condition, all nodes are sifted to select a node candidate set C1 which satisfiesthe condition that c(primary set)>Sigmac(subsidiary set), and nodes in the C1 are subjected to secondary noise adding, so that the key mode satisfies the differential privacy. By conducting noise adding twice, on the premise that the safety is satisfied, the effectiveness can be improved. Besides, for window sliding, to improve reasonable distribution and privacy budget of the mining time, two differential nodes are utilized to calculate and decide noise adding situations, counting is considered, and the branch number condition is also considered, so that the mined key mode lowers the computing complexity as much as possible on the condition that the safety is guaranteed and is for study personnel to conduct analysis work.

Differential privacy protecting method of datastream key mode mining

Security is very important in modern life due to most information is now stored in digital format. A good security mechanism will keep information secrecy and integrity, hence, plays an important role in modern information exchange. However, cryptography algorithms are extremely expensive in terms of execution time. To make data not easily being cracked, many arithmetic and logical operations will be executed in the encryption/decryption process with many data movement. This means the cryptographic applications are both computation and memory intensive. Using a general-purpose processor for this scenario would not be very cost-effective. This study addresses this problem. Compared to the previous designs, we used a performance analyzer to identify “hotspot” functions across a set of benchmarks. The hotspot function consumes a substantial amount of the execution time of the specific algorithm. Then we translate these hotspot functions into hardware accelerators to improve the performance. Overall we achieve 34 – 83 folds of speedup.

/pdf/the-performance-analysis-and-hardware-acceleration-of-crypto-3ldoazpj9q.pdf

The Performance Analysis and Hardware Acceleration of Crypto-computations for Enhanced Security

With the era of many-core processing upon us, it is important to be able to effectively utilize the abundant number of cores available in the processor. Given multiple programs to be run simultaneously, one must decide not only how many cores to allocate to each program, but also at what frequency and voltage to run them. Furthermore, the goal of producing the best performance and the lowest energy consumption or the lowest energy delay product (EDP), without any knowledge about the programs, this can be a challenging task. For programs that can take advantage of multiple cores, what we observed is that the largest benefit in reducing the execution time and EDP is by allowing the program to use additional cores. This execution time advantage can follow an exponentially decaying function, thus providing an effective mechanism in determining the number of cores to use for each program. In this paper we introduce a heuristic method - GreedyTalents - to accomplish this task based on the empirical observation of the execution time of the running programs. We also show that GreedyTalents provides equal or better results than other comparable methods, and converges faster in determining the settings to use, showing it as a promising method of energy-aware auto-tuning for many-core processors.

GreedyTalents: An Energy-Aware Auto-Tuning Method for Many-Core Processor

SDN is a new network operation and management solution, which decouples the control and data planes in a communication network SDN makes it easier for network administrators to create, modify, and manage dynamic networks by abstracting low level functions and network structure Generally, SDN uses a centralized controller, which offers a global view of the entire network This new feature offers the flexibility for administrators to define the strategies in terms of how the network flow is forwarded on a software level With the advancing of the research on SDN, however, distributed SDN controllers are also introduced by researchers One reason for the emerging of distributed SDN controller is: it can address the problems of scalability, reliability, and performance issues that a centralized SDN controller suffers from acceleration

Chen Liu

Papers

Differential privacy protecting method of datastream key mode mining

The Performance Analysis and Hardware Acceleration of Crypto-computations for Enhanced Security

GreedyTalents: An Energy-Aware Auto-Tuning Method for Many-Core Processor

GPU-based acceleration of SDN controllers

Distributed workflow oriented data stream scheduling method and system