The Tenth International Symposium on Networking and Computing (CANDAR 2022) was held on November 21st and 22nd, 2022. The organizers of CANDAR 2022 invited authors to submit extended versions of the papers presented at the symposium. Consequently, a total of 19 articles were submitted for consideration in this special issue. Out of these submissions, 10 papers have been accepted and are included in this issue as extended versions. This issue owes its success to the dedicated efforts and expertise of numerous individuals who generously contributed their time and knowledge in handling the submitted papers. We extend our heartfelt gratitude to the guest editors for their exceptional work in conducting the review process. Special thanks go to: Professor Hideharu Amano, Professor Shuichi Ichikawa, Professor Ryo Nakamura, Professor Nobuhiko Nakano, Professor Toru Nakanish, rofessor Yasuyuki Nogami, Professor Fukuhito Ooshita, Professor Hiroyuki Sato, Professor Chisa Takano, and Professor Takashi Yokota. Their valuable contributions ensured the quality and rigor of the review process, making this issue possible. We would also like to express our sincere appreciation to the anonymous reviewers who diligently reviewed the papers and provided insightful comments and suggestions to enhance the quality of the submissions. Their thorough and thoughtful evaluation played a vital role in shaping the content of this special issue. We are truly grateful for their invaluable contributions, as this issue would not have been possible without their dedicated efforts. In conclusion, we are immensely grateful for the collective efforts of all individuals involved in making this special issue a reality. From the organizers, guest editors, and anonymous reviewers, each person has contributed significantly. Their dedication and support have enabled us to present this collection of extended papers, showcasing advancements in networking and computing. We hope this issue inspires further innovation in the field and serves as a valuable resource for researchers and practitioners.

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Owing to the rise in mobile users accessing high-quality video content, content delivery networks are under increased load. Therefore, a distributed cooperative caching technique, in which each mobile device functions as a cache server and shares a cache through device-to-device communication has been proposed to alleviate the network load. However, efficient content placement on mobile devices is challenging, because the cache capacity of each mobile device is limited and did not remain at a fixed location. In this study, we propose a load-based content allocation (LBCA) method that efficiently distributes cache data to mobile devices based on the load of the base station (BS). The proposed scheme uses scalable video coding (SVC) that subdivides the content data hierarchically and dynamically selects the video quality depending on the network load. Additionally, we propose a multi-stage LBCA (M-LBCA) that divides the BS distribution area into concentric clusters to efficiently manage cache capacity for SVC data while considering the distance from the BS. Simulations demonstrated a decrease in the total number of users unable to continually view content on congested networks when using LBCA and M-LBCA as compared to the existing method in four evaluation environments. Therefore, our experiments demonstrate that the proposed cache control scheme improved the user’s quality of experience.

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The security of Schnorr signature Sch has been widely discussed so far. Recently, Fuchsbauer, Plouviez and Seurin gave a tight reduction that proves EUF-CMA of Sch in the random oracle (ROM) with the algebraic group model (AGM) from the discrete logarithm (DL) assumption at EUROCRYPT 2020. Kiltz, Masny and Pan considered multi-user security of Sch at CRYPTO 2016, whereas Fuchsbauer et al. considered the single-user security only. More precisely, Kiltz et al. constructed a tight reduction from EUF-CMA to MU-EUF-CMA. Combining these two results will likely enable us to construct a tight reduction that proves MU-EUF-CMA security of Sch in AGM+ROM from DL assumption. Against such an intuition, we show an impossibility on proving MU-EUF-CMA of Sch in AGM+ROM only by combining them in this paper. To estimate our impossibility result, we also discuss why the result by Fuchsbauer et al. cannot be applied to MU-EUF-CMA setting. Our result therefore suggests that we are required to develop a new proof technique beyond the algebraic reduction or to find a new form of public keys other than that considered in our impossibility, in order to show MU-EUF-CMA of Sch in AGM+ROM.

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Membrane computing, which is also known as a P system, is a computational model inspired by the activity of living cells. Several P systems, which work in a polynomial number of steps, have been proposed for solving computationally hard problems. However, most of the proposed algorithms use an exponential number of membranes, and reduction of the number of membranes must be considered in order to make a P system a more realistic model. In the present paper, we propose asynchronous P systems based on the Bron-Kerbosch algorithm for solving the maximum clique problem with fewer membranes. The proposed P systems solve the maximum clique with n vertices in O(n^2) parallel steps or O(n^2 2^n) sequential steps. We evaluate the number of membranes used in the proposed P systems by comparing with the numbers of membranes used in known P systems. Our experimental results demonstrate the validity and efficiency of the proposed P systems.

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In recent years, the demand for Internet of Things (IoT) devices has increased dramatically. They are used in a variety of applications, such as sensors, in-vehicle terminals, control devices in factories, and medical equipment in hospitals. However, attackers are also increasingly targeting IoT devices, making it necessary to implement security countermeasures. However, IoT devices rarely have sufficient resources like high-end products such as PCs and servers. Therefore, we are developing a system that can detect malware with lower resources. In this study focusing on targeting the RISC-V architecture, we measure the classification accuracy of a detection mechanism using random forests and reduce the circuit scale and power consumption. We show that the detection mechanism can be made more accurate in classifying normal programs and malware and cost of the detection mechanism can be reduced.

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The group signature with message dependent opening (GS-MDO) is a variant of the group signature in the sense that the opening authority is split into two parties called the opener and the admitter. Most known constructions of GS-MDO consider the static model. The only scheme using the dynamic model by Sun and Liu has a problem of the anonymity against the admitter in the real-world usage because the signing process requires the interaction between the signer and the admitter. In this paper, we restart the line of research of GS-MDO in the dynamic setting. We introduce the definition of the dynamic group signature with message dependent opening (DGS-MDO) with the security requirements and propose a generic construction. By instantiating our construction with appropriate primitives, we can obtain a DGS-MDO scheme with the standard model security, constant signature size and non-interactive signing process.

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Statistical processing and Artificial Intelligence (AI) development utilizing big data have been actively researched recently. However, there are growing concerns about privacy violations due to the use of private data. For such concerns, the EU General Data Protection Regulation (GDPR) was introduced to regulate the handling of personal information. The GDPR makes it difficult to discover medical knowledge through big data analysis in medical studies. However, the GDPR is not concerned with handling non-personally identifiable statistical information. Statistical information is commonly published, collected, and analyzed. However, it is unknown whether collecting and analyzing such statistical information can generate medical evidence through variable-to-variable research, such as the relationship between tobacco and cancer. In this paper, we propose to use statistical information that is not concerned by the GDPR to estimate cross-tabulation tables, which are usually generated from personal information in medical research and are widely used for analysis between medical variables. In particular, as statistical information, we use “patient characteristics” formatted data commonly published in medical research. The scope of this paper is the situation where the publisher of statistical information and the analyst of published statistical information differ. On the publisher side, we assume the publisher collects raw data from a target people group by random sampling multiple times and converts the data to patient characteristics formatted data. On the analyst side, we assume the analyst collects those published many random sampled patient characteristics formatted data and estimates the cross-tabulation table by the Law of Large Numbers (LLN). We model the publisher-analyst situation described above. In the aforementioned model, we evaluate our proposal estimation’s usefulness through both theoretical and experimental accuracy assessments. Furthermore, for quantitative Privacy Preserving Data Mining (PPDM), we evaluate the risk of anonymity when collecting multiple patient characteristics using the existing anonymity indicator, the Patient Family Detect on Overall Category (PFDOC) entropy. We theoretically and experimentally check the occurrence rate of vulnerable patient characteristics with PFDOC entropy equal to zero obtained by the analyst. In the experiment, the target people group data is 20,000 personal data which have four categorical binary values. As the publisher model, we created 10,000 patient characteristics, which are statistics for randomly sampled 50 data from the 20,000 data. As the analyst model, we estimated the cross-tabulation table by the 10,000 patient characteristics. The theoretical prediction error was 1.8% (95% CI), and the experimental error was within 1.5% (95% CI, n = 100), indicating a close agreement between theory and experiment. Regarding anonymity, it was theoretically expected that PFDOC entropy = 0 patient characteristics would be rare in categories with a population ratio of 25% to 75%, leading to ensured anonymity. It was confirmed in the experiment. Based on these results, we can conclude that, by using the patient characteristics formatted data release and collection model and selecting the appropriate population ratio categories, an analyst can accurately estimate cross-tabulation tables while preserving PFDOC entropy-based anonymity without legal restriction.

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In a drone logistics system, a message delivery system in which the drone delivers messages for other users on the way to a parcel delivery destination has been proposed. To reduce the complexity of message delivery routes, this paper proposes a message delivery method that divides logistics areas and determines the message delivery routes in each area. The method also makes it possible for a later departure drone and an early departure drone to exchange information to add, cancel, and/or exchange their messages and delivery points. The simulation experiments show that compared with the previous method, the proposed method has lower computational complexity and in contrast, the average travel distance increases by a maximum of 53.6% to 77.9% and so forth.

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Pairing is carried out by two steps, Miller loop and final exponentiation. In this manuscript, the authors propose an efficient Miller loop for a pairing on the FK12 curve. A Hamming weight and bit-length of loop parameter have a great effect on the computational cost of the Miller loop. Optimal-ate pairing is used as the most efficient pairing on the FK12 curve currently. The loop parameter of optimal-ate pairing is 6z + 2 where z is the integer to make the FK12 curve parameter. Our method uses z which has a shorter bit-length than the previous optimal-ate pairing as the loop parameter. Usually, z has a low Hamming weight to make final exponentiation efficient. Therefore, the loop parameter in our method has a lower Hamming weight than the loop parameter of the previous one in many cases. The authors evaluate our method by the number of multiplications and execution time. As a result, the proposed algorithm leads to a 3.71% reduction in the number of multiplications and a 3.03% reduction in the execution time. In addition, the authors implement other STNFS secure curves and evaluate these curves from viewpoint of execution time.

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In addition to their playing skills, AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games if we define new measures based on their raw output. For the ancient game of Go, we develop a numerical tool for context-sensitive move-by-move performance evaluation and for automating the recognition of game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. In this paper, we investigate the properties of this measure and describe some applications for developing learning tools and analyzing a large number of games. As we use AI tools to gain new insights into the ancient game of Go and develop a more precise, quantified understanding, this work fits into the more significant and general project of utilizing superhuman AI engines for deepening human understanding and growing human knowledge.

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The behavior of virtual machine (VM) programs are monitored by virtual machine monitors (VMMs) for security purposes. System calls are frequently used as a monitoring point. To monitor the system calls, the VMM inserts a breakpoint, called a hook point, into the memory of the monitored VM. The hook points are determined based on experimental knowledge. However, reading the source codes of operating systems (OSes) requires specialized knowledge. In addition, the appropriate hook point differs among OSes and OS versions. Analyzing the source code in each OS update is impractical. Searching for the appropriate hook point for various OSes is also difficult. To address these problems, we propose a method for estimating the hook point using a memory analysis technique. The proposed method acquires the memory of the monitored VM and then searches for an appropriate instruction appropriate to hook. The search instructions depend on the processor architecture. In addition, we also proposed a method for searching the appropriate instruction using a single step execution. This version reduces the cost for searching the instructions and improve robustness for various Linux versions. The experimental results showed that the proposed method precisely estimates the hook point for various OS versions and OSes. In addition, the overhead of the proposed method is small, considering the boot time of the monitored VM.

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