A Visualization System for Analyzing Information Leakage

This work explores the potential of revenue-sharing contracts to facilitate information sharing in a supply chain and mitigate the negative effects of information leakage. We consider a supplier who offers a revenue-sharing contract to two competing retailers, one of whom has private information about uncertain market potential and orders first. This order information may be leaked to the uninformed retailer by the supplier to realize higher profits. We show that the incentives of the supplier and retailers are better aligned under a revenue-sharing contract, as opposed to under a wholesale-price contract, reducing the supplier's incentive to leak. This is true for a wide range of wholesale prices and revenue-share percentages and is more likely when the revenue-share percentage is higher and when variation in demand is greater. Preventing information leakage may result in higher profits not only for the informed retailer and supplier but surprisingly even for the uninformed retailer. Our results are robust when the model is generalized along various dimensions. This paper was accepted by Yossi Aviv, operations management.

The security of cryptographic devices is gaining significance, of late, owing to their important role in ensuring information security in the internet of things (IoT). Amongst various implementation attacks, the laser and electromagnetic (EM) fault-based attacks are considered the most powerful because of their control over the injection timing and position. This paper discusses a practical countermeasure against fault attacks, focusing on a secure measure to prevent information leakage, in particular, after a fault is detected. Preliminary experiments using a field programmable gate array (FPGA) board show that cutting off the power supply (VDD) could be an effective counter-measure for preventing information leakage from cryptographic devices under the fault attacks.

With the spread of information on the Internet and the explosive growth of intellectual property information, the traditional intellectual property management model relying on third-party institutions cannot meet the demand for intellectual property protection, which has a cumbersome process, low efficiency, and insufficient evidence of rights protection. To address the issues of information falsification and leakage, we present an anonymous and traceable intellectual property management system based on blockchain, namely ATIPM, which employs the non-interactive zero knowledge proof to realize user unlinkability and anonymous transactions to protect the users’ intellectual property information. To avoid a single point of accountability, the ATIPM introduces a threshold ramp secret sharing scheme to achieve the traceability of intellectual property for all users and greatly improve the users’ privacy security and autonomy by preventing information leakage from malicious third-party institutions. Furthermore, the ATIPM can improve the management efficiency of intellectual property by utilizing smart contracts to realize efficient retrieval and verification of intellectual property. The evaluation results demonstrate the effectiveness of our proposed system.

Multi-tenancy of the cloud maximizes the utility of computation and storage resources by multiplexing the underlying hardware infrastructure amongst cloud customers; however, it also introduces significant security issues such as information leakage between two virtual machines (VMs) even if certain access control policy (e.g., Chinese Wall security policy) has been deployed in the cloud. Physical resource isolation between VMs is an effective mechanism to remove the covert channels in the cloud and prevent information leakage; however, due to economic concerns or negligence, some cheap-and-lazy cloud providers are not motivated to enforce the physical resource isolation as they promised. In this paper, we first develop a mechanism to check the co-residency of two files on local hard disk(s) by measuring the file access time, and then extend our mechanism to check data storage co-residency on Amazon S3 cloud storage.

A novel controlled bidirectional quantum secure direct communication (CBQSDC) protocol based on Grover’s algorithm is proposed in our paper, which is on the basis of investigating the properties of it. To avoid information leakage, the strategy of adding control-not gate is employed in our protocol. The proposed protocol uses Bell state as quantum resource to transmit information in a dual-quantum-channel. Through the proposed measures, we can not only prevent information leakage, but also improve the efficiency of transmission. As can be shown from the security analysis, the Grover’s algorithm has great application prospects in the research of CBQSDC.

Since smart devices such as smart phones and tablets are at high risk of theft, they prevent information leakage from storage by full disk encryption (FDE). Recently, however, information leakage from memory is being critical as non-volatile main memory (NVMM) is emerging. In smart devices with NVMM, even if storage is encrypted, sensitive data in the page cache can be stolen by physical attacks. In this paper, we propose efficient page-cache encryption called Cache-Crypt to prevent part of the storage data from leaking via the page cache. Cache-Crypt is well integrated with FDE to avoid redundant cryptographic operations and take advantage of FDE. It bypasses encryption and decryption by FDE and stores encrypted data in the page cache. In response to application's requests, it executes encryption and decryption using FDE's cryptographic mechanism. To reduce the overhead, Cache-Crypt defers re-encryption of decrypted data temporarily. We have implemented Cache-Crypt in Linux and confirmed that the performance was comparable to that in FDE only.

A Differential Power Analysis (DPA) attack is an exploit which overcomes the hardware and software security by analyzing the correlation between electricity usage of a chip in a device and the encryption key. These attacks are non-invasive, leading an intruder to crack a system without leaving any trace. CMOS Technology is prone to DPA attack because of higher power dissipation. A DPA resistant adiabatic technique has been analyzed which has lower power dissipation when compared to the CMOS technology. The conventional Positive Feedback Adiabatic Logic (PFAL) suffers from certain non-adiabatic energy loss. Hence, Energy Efficient Secure Positive Feedback Adiabatic Logic (EE-SPFAL) is proposed which reduces the non-uniform power consumption, preventing information leakage thus securing the system from DPA attacks. Thus the proposed EE-SPFAL is used to design logical circuits such as buffer, OR-XNOR and AND-NAND gate. It is proved that the information leakage in the form of current consumption takes place in PFAL buffer whereas current consumption traces in EE-SPFAL circuits are uniform, depicting the ability of the proposed logic family to resist DPA attack. The simulation was done using Cadence virtuoso 180 nm technologies.

Personal Information Overload Affects the Perceived Risk and Information Leakage Prevention

The deployment of cloud infrastructure in libraries enhances scalability and availability of resources, but different hazards have evolved, and so has the need for sound security measures for shielding critical information from some risks such as quantum computing’s risks. This paper proposes the Quantum-Resistant Federated Authentication (AQFA) framework which uses the federated learning with quantum-resistant cryptographic technologies for the improvement of authentication in library cloud systems. AQFA employs a multilayer security model which ensures two things; user credential protection against quantum attacks, privacy, and accessibility. It also uses trust models to adapt its incident response to real-time user behavior; thereby making it easier to manage risks of unauthorized access. In addition, AQFA also adapts homomorphic encryption to help encrypt data for processing with the ability of library that enables libraries to perform operations on encrypted data. This thinking solution not only improves the general security of library cloud infrastructures but also enables reasonable scale and user adjustment in a more and more digital environment. The findings further affirm the ability of AQFA in mitigating risks to historical library assets and preventing information leakage in cloud configuration

Information Flow Control (IFC) is a method of enforcing confidentiality by using labels, data structures for specifying security classifications. IFC is used in programming languages to monitor procedures in an attempt to detect and prevent information leakage. While it ensures greater security, IFC excessively restricts flow of information. This paper presents a model of information flow control using semi-discretionary label structures. We propose a set of rules that not only increases the flexibility of IFC but also defines labels as a practical component of a security system. We propose a dynamic approach using a centralized model for dynamic label checking and verify the proposed model using theoretical proofs.

Predicting interfacial tension in brine-hydrogen/cushion gas systems under subsurface conditions: Implications for hydrogen geo-storage

RFID is an important technology that can be used to create the ubiquitous society. But an RFID system uses open radio frequency signal to transfer information and this leads to pose many serious threats to its privacy and security. In general, the computing and storage resources in an RFID tag are very limited and this makes it difficult to solve its secure and private problems, especially for low-cost RFID tags. In order to ensure the security and privacy of low-cost RFID systems we propose a lightweight authentication protocol based on Hash function. This protocol can ensure forward security and prevent information leakage, location tracing, eavesdropping, replay attack and spoofing. This protocol completes the strong authentication of the reader to the tag by twice authenticating and it only transfers part information of the encrypted tag’s identifier for each session so it is difficult for an adversary to intercept the whole identifier of a tag. This protocol is simple and it takes less computing and storage resources, it is very suitable to some low-cost RFID systems.

This study employs a quantitative, cross-sectional, multi–case design to evaluate determinants of workplace accident compensation outcomes through harmonized electronic health records (EHRs), administrative claims, and a brief Likert-scale survey capturing process and communication constructs. The investigation encompasses multiple compensation jurisdictions and health systems, enabling comparative analysis and enhancing external validity. Closed adjudicated cases serve as the analytic unit, providing consistent labeling for four key outcomes, approval status, benefit magnitude, processing time, and appeal occurrence, each reflecting distinct stages of the adjudicative lifecycle. Data integration occurs under a privacy-preserving linkage protocol across three coordinated sources: structured EHR extracts containing diagnostic codes, procedures, medications, laboratory results, and encounter timestamps; claims system data containing lodgment and decision dates, indemnity and medical payments, attorney involvement, and prior claims indicators; and survey data capturing process-level constructs such as documentation completeness, communication quality, transparency, and adjudication clarity. Rigorous data quality assessments, reproducible phenotyping of clinical and administrative variables, and standardized reporting protocols ensure methodological transparency. Missingness is characterized and resolved through multiple imputation or indicator approaches, outliers are managed via clinically justified bounds, and all variables are timestamped relative to the index event to maintain temporal coherence and prevent information leakage. The analytical framework combines explanatory and predictive modeling to investigate the interplay between clinical burden, process efficiency, and adjudicative outcomes. Logistic regression models estimate approval and appeal likelihoods, generalized linear models with Gamma family and log link address skewed benefit magnitudes, and Cox proportional hazards models quantify time-to-decision processes. Model performance is evaluated using discrimination (AUC, precision–recall AUC, F1 score), calibration (Brier score, calibration slope), and equity diagnostics across subgroups defined by age, sex, industry, and language. Predictor domains include clinical severity, comorbidities, medication exposure, care intensity, process metrics, prior claims, and demographic–occupational context, complemented by the four Likert-based process indices. Reproducibility is ensured through version-controlled scripts, documented variable definitions, and auditable preprocessing pipelines that promote transparency and external validation. Collectively, the study advances a robust, equity-aware methodological framework for assessing and predicting compensation adjudication outcomes using EHR-linked data—bridging clinical analytics with administrative decision-making to enhance fairness, interpretability, and accountability in occupational health research.

In the past few years, smart healthcare has become a popular topic. Many users wear smart devices to monitor their health conditions. To provide information for health analysis, many sensors are installed in smart wearable devices to collect personal health data. Due to the limitation of space and computational power, private health data is not processed locally. Instead, it is usually sent to a mobile phone for further analysis, which required wireless data exchange between the mobile phone and smart wearable devices. To protect a user’s privacy, it is important to guarantee the connection security of the devices’ network as well as prevent information leakage. A possible method to secure the data exchange process is symmetric encryption. In this paper, we investigate the feasibility of symmetric key generation for communication between a mobile phone and smart wearable device using angular velocity data collected by gyroscopes as data source. We collected over 1000 samples of gait data, totally more than 20000 seconds of movements, using two industrial products including a smart watch and mobile phone placed on wrist and in pocket respectively. We successfully generated the same random number for mobile phone and smart wearable device in 78% samples.

With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.