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 provides an initial scheduling method and system for distributed workflow oriented data flow. The method comprises the steps that: 1) the dependence relationship between activities and data is determined according to the frequency that the activities use the data; 2) the number of activities depending on any two data is determined according to the dependence relationship of activities and data; 3) an cooperative relationship matrix is built with the number of activities depending on any two data as the element, and the row and column exchange of the cooperative relationship matrix is carried out to obtain close mutual relationship between close rows and columns; 4) the cooperative relationship matrix after exchange is partitioned so that the data jointly used by any activity coexist in a subblock, and data corresponding to each subblock form a data set; and 5) the data sets only containing movable data are scheduled to a data set storage bank needing shortest flow time. With the method and the system, data flow time between business processes of geographical distribution as well as the frequency that a workflow engine can not run due to network interruption is effectively reduced.

Initial scheduling method and system for distributed workflow oriented data flow

The modern era of computing involves increasing the core count of the processor, which in turn increases the energy usage of the processor. How to identify the most energy-efficient way of running a multiple-program workload on a many-core processor while still maintaining a satisfactory performance level is always a challenge. Automatic tuning on the voltage and frequency level of a many-core processor is an effective method to aid solving this dilemma. The metrics we focus on optimizing are energy usage and energy-delay product (EDP). To this end, we propose SVM-JADE, a machine learning enhanced version of an adaptive differential evolution algorithm (JADE). We monitor the energy and EDP values of different voltage and frequency combinations of the cores, or power islands, as the algorithm evolves through generations. By adding a well-tuned support vector machine (SVM) to JADE, creating SVM-JADE, we are able to achieve energy-aware computing on many-core platform when running multiple-program workloads. Our experimental results show that our algorithm can further improve the energy by 8.3% and further improve EDP by 7.7% than JADE. Besides, in both EDP-based and energy-based fitness SVM-JADE converges faster than JADE. Parallel tree skeletons are basic computational patterns that can be used to develop parallel programs for manipulating trees. In this paper, we propose an efficient implementation of parallel tree skeletons on distributed-memory parallel computers. In our implementation, we divide a binary tree to segments based on the idea of m-bridges with high locality, and represent local segments as serialized arrays for high sequential performance. We furthermore develop a cost model for our implementation of parallel tree skeletons. We confirm the efficacy of our implementation with several experiments.

/pdf/automatic-tuning-on-many-core-platform-for-energy-efficiency-1gz5ai3hss.pdf

Automatic Tuning on Many-Core Platform for Energy Efficiency via Support Vector Machine Enhanced Differential Evolution

Protecting data and its communication is a critical part of the modern network. The science of protecting data, known as cryptography, uses secret keys to encrypt data in a format that is not easily decipherable. However, most commonly secure logons for a workstation connected to a network use passwords to perform user authentication. These passwords are a weak link in the security chain, and are a common point of attack on cryptography schemes. One alternative to password usage for network security is to use a person’s physical characteristics to verify who the person is and unlock the data correspondingly. This study focuses on the Cambridge biometric cryptosystem, a system for performing user authentication based on a user’s iris data. The implementation of this system expanded from a single-core software-only system to a collaborative system consisting of a single core and a hardware accelerator. The experiment takes place on a Xilinx Zynq-7000 All Programmable SoC. Software implementation is performed on one of the embedded ARM A9 cores while hardware implementation makes use of the programmable logic. Our hardware acceleration produced a speedup of 2.2X while reducing energy usage to 47.5 % of its original value for the combined enrolment and verification process. These results are also compared to a many-core acceleration of the same system, providing an analysis of different acceleration methods.

/pdf/hardware-accelerator-approach-towards-efficient-biometric-33y317oqy4.pdf

Hardware Accelerator Approach Towards Efficient Biometric Cryptosystems for Network Security

For scientific research and business decision purposes, the relational and transaction data often needs to be published. Because individuals’ privacy exists in this data, it needs to be appropriately anonymized before to be released. However, the privacy model (k, km)-anonymity cannot preserve the individual’s privacy with more than m items in transaction attribute. Also, it does not make the diversity constraint and cannot prevent attribute disclosure. Moreover, it considers all items be sensitive in the transaction attribute, which will cause serious information loss. In this paper, the (k, ρ)-anonymity model is proposed, which can solve the problem caused by (k, km)-anonymity. Then an anonymous approach is designed to achieve (k, ρ)-anonymity. Experiment results show that our approach is better than existing anonymous approach for publishing relational and transaction data in utility and security.

A New Approach for Anonymizing Relational and Transaction Data

Protecting data is a critical part of life in the modern world. The science of protecting data, known as cryptography,
makes use of secret keys to encrypt data in a format that is not easily decipherable. However, most modern cryptography
systems use passwords to perform user authentication. These passwords are a weak link in the security chain, as well as
a common point of attack on cryptography schemes. One alternative to password usage is biometrics: using a person’s
physical characteristics to verify who the person is and unlock the data correspondingly. This study provides a concrete
implementation of the Cambridge biometric cryptosystem. In addition, hardware acceleration has been performed on the
system in order to reduce system runtime and energy usage, which is compared with software-level code optimization.
The experiment takes place on a Xilinx Zynq-7000 All Programmable SoC. Software implementation is run on one of
the embedded ARM A9 cores while hardware implementation makes use of the programmable logic. This has resulted in
an algorithm with strong performance characteristics in both energy usage and runtime.

Chen Liu

Papers

Initial scheduling method and system for distributed workflow oriented data flow

Automatic Tuning on Many-Core Platform for Energy Efficiency via Support Vector Machine Enhanced Differential Evolution

Hardware Accelerator Approach Towards Efficient Biometric Cryptosystems for Network Security

A New Approach for Anonymizing Relational and Transaction Data

Implementation and optimization of a biometric cryptosystem using iris recognition