The increasing reliance on large-scale data analysis raises critical concerns regarding protecting sensitive personal information and regulations like GDPR. While anonymization techniques and contractual agreements offer some safeguards, they do not provide data providers with control over datasets and often hinder data’s flexible and advanced use. This paper introduces "Analysis Control" and its implementation in the "yak" platform, designed to address these challenges. yak enables data providers to define and enforce policies that govern data provisioning and analysis processes, ensuring data confidentiality and controlled utilization. By employing a modular architecture based on WebAssembly and operating within a Trusted Execution Environment, yak allows for secure and extensible analysis. Data providers can verify the platform’s integrity through attestation protocols and control analysis workflows through policies that control analysis function executions and data anonymization. Evaluations using synthetic datasets demonstrate yak’s feasibility, with negligible overhead, and confirm its ability to enforce data provider policies.

Community extraction is widely used for various applications concerning network analysis, but its scope has mostly been confined to single-layer network. However, the real-world data are frequently represented as multilayer network, and furthermore, each layer frequently exhibits different properties. It is thus critical to be able to control to what degree each layer should be reflected in the overall network, in order to perform a reliable community extraction. In this paper, we propose a superimposition method called WAPPRS (weighted APPR with restart) that can monotonically increase the degree of reflection and handle a wide range of values for reflection. Our experiments demonstrate that our proposed model outperforms previous models when dealing with the properties desirable for reliable community extraction from multilayer networks.

Top-k Personalized PageRank (PPR) is a graph analysis method used to determine the$k$most important nodes with respect to a source node. To realize fast Top-k PPR computation, indexing for each node is effective. When we apply the index-based Top-k PPR methods to dynamic graphs, the index becomes stale with edge updates, and index correction is required. Although the existing methods perform index correction for every update to guarantee Top-k PPR accuracy, they involve heavy re-indexing computation or significant memory overhead. This paper proposes a method that achieves comparable accuracy to guaranteed methods while significantly reducing re-indexing by focusing on the fact that index references are concentrated on the nodes whose index is unlikely to change due to edge updates. In particular, our method omits re-indexing as long as we achieve comparable accuracy. Furthermore, our method involves the minimum memory overhead among the existing index-based methods. The space complexity of the index is$\Theta (n + m)$, where$n$and$m$are the number of nodes and edges of the graph, respectively. The evaluation results using real-world datasets show that our method achieves more than 0.999 Normalized Discounted Cumulative Gain until 20% of edges are updated from index generation.

Containers are the technology for Linux to isolate execution environments. By distributing a container image, which is a collection of files contained in the container, users can use an execution environment that includes the necessary files and libraries. However, container images are tens to hundreds of megabytes in size and require many network resources to be transferred. Especially in low-bandwidth network environments like edge computing, frequent image updating can be difficult and affect other services’ communication. In this paper, we propose a method to reduce the data size required for image updates using delta encoding. We use delta encoding to reduce data size and finish updating quickly, but generating and applying deltas is a time-consuming operation. Our method proposes DeltaMerging which enables faster delta generation by merging existing deltas, and Di3FS which applies deltas lazily. The proposed method reduces the data size required to update container images from 5 to 40% of that of existing methods. Also, the time required to generate and apply deltas is greatly reduced with DeltaMerging and Di3FS. Furthermore, the performance degradation of the application in the container was almost negligible.

Abstract In many Internet of Things (IoT) applications, data sensed by an IoT device are continuously sent to the server and monitored against a specification. Since the data often contain sensitive information, and the monitored specification is usually proprietary, both must be kept private from the other end. We propose a protocol to conduct oblivious online monitoring—online monitoring conducted without revealing the private information of each party to the other—against a safety LTL specification. In our protocol, we first convert a safety LTL formula into a DFA and conduct online monitoring with the DFA. Based on fully homomorphic encryption (FHE), we propose two online algorithms (Reverse and Block) to run a DFA obliviously. We prove the correctness and security of our entire protocol. We also show the scalability of our algorithms theoretically and empirically. Our case study shows that our algorithms are fast enough to monitor blood glucose levels online, demonstrating our protocol’s practical relevance.

In this paper, we describe learner’s behavior detecting system for mentoring in e-Learning. It is important for learner to keep her/his motivation in e-Learning. Therefore, existing e-Learning system and LMS (Learning Management System) is to realize mentoring for learner based on learning history. However, past mentoring cannot cover in learner’s learning. Then, we focus on the learner’s behavior in her/his learning. We propose and develop a learner’s behavior detection system for mentoring.

This paper proposes a new control system for bilateral telemanipulators to overcome one of the severest problems, the instability in the presence of time delay in data transmission. The control system proposed is based on the impedance control with virtual internal models (VIMs) driven by the sum of two forces detected on a master and a slave. The sufficient condition for the stability with arbitrary time delay is given based on small gain theorem. The sufficient condition can be satisfied by choosing appropriate parameters of the VIMs. Consequently, the control system is stable under any time delay. The proposed control system is applied to an experimental teleoperation system, which consists of two manipulators different in size. Data for teleoperation are transmitted through wireless LAN (256 kbps, max.). With this system, a stable contact task (to teach a desired position of the slave) was accomplished even with the time delay of 0.5 second. And it has been experimentally verified that the system is stable when time delay varies at random or when data transmission ceases and resumes.

This study focuses on a mobile robot guidance control system in which a mobile robot tracks the reference trajectory parallel to a guide wall. A laboratory mobile robot which has two driving wheels and two distance measurement sensors on each side to measure distances from the guide wall is considered. Because measured distances are influenced by noise such as guide wall undulations and mobile robot vibrations, a nonlinear observer based on the extended linearization method is adopted to estimate state variables in mobile robot motion dynamics. A mobile robot heading angle and lateral deviation estimation algorithm based on this nonlinear observer are proposed, and the observer stability condition is derived. The estimation algorithm combined with state feedback controller comprises the mobile robot guidance control system. Estimation and control performance of the system are evaluated with a simulation model and the laboratory mobile robot.