Data Masking Generic Model

Logging is an essential software practice that is used by developers to debug, diagnose and audit software systems. Despite the advantages of logging, poor logging practices can potentially leak sensitive data. The problem of data leakage is more severe in applications that run on mobile devices, since these devices carry sensitive identification information ranging from physical device identifiers (e.g., IMEI MAC address) to communications network identifiers (e.g., SIM, IP, Bluetooth ID), and application-specific identifiers related to the location and the users' accounts. This preliminary study explores the impact of logging practices on data leakage of such sensitive information. Particularly, we want to investigate whether log-related statements inserted into an application code could lead to data leakage. While studying logging practices in mobile applications is an active research area, to our knowledge, this is the first study that explores the interplay between logging and security in the context of mobile applications for Android. We propose an approach called MobiLogLeak, an approach that identifies log statements in deployed apps that leak sensitive data. MobiLogLeak relies on taint flow analysis. Among 5,000 Android apps that we studied, we found that 200 apps leak sensitive data through logging.

Generic thermal model of electrical appliances in thermal building: Application to the case of a refrigerator

Staff compliance is a crucial factor in maintaining the confidentiality, integrity, and availability of patient data, in line with the Minister of Health Regulation No. 269/2008 and the ITE Law No. 11/2008. EMR also increases the risk of sensitive patient data leakage if not managed with strict security procedures. Annisa Hospital Bogor, as an institution that has implemented EMR, is obliged to ensure the confidentiality, integrity, and availability of data. This study aims to measure and analyze the level of compliance of medical and non-medical staff at Annisa Hospital Bogor with information security procedures in managing Electronic Medical Records (EMDR). The method used is descriptive quantitative by distributing questionnaires to the EMR user staff population. The results showed that the average compliance rate was 81.6%, but vulnerabilities were found in the use of passwords and log-out protocols after accessing the system. The study recommends the need for regular training and systematic audits to improve staff awareness and discipline.

An Improved Data Leakage Detection System is designed to mitigate the leakage of crucial and sensitive data in a cloud computing environment. Generally, leakage of data in computing system has caused a lot of irreparable damage or catastrophe to various institutions or organizations worldwide. Therefore, this research aims at detecting and preventing any intentional or non-intentional data leakages using dynamic password or key for data decryption security mechanisms. To achieve this the OOADM methodology was adopted. The new system was implemented using ASP.net MVC and Microsoft SQL Server Management Studio as the backend. And by incorporating an Audit trail/Transaction log mechanism, the new system monitors the activities within and outside the computing environment with date and time stamp. Hence, the system can be applied in any environment for the prevention and detection of any data leakage.

In this study, we present the uitHyDroid system, which allows the detection of sensitive data leakage via multi-applications using hybrid analysis. uitHyDroid uses static analysis to collect user interface elements that must interact to illuminate possible sensitive data flows. In addition, dynamic analysis is used to capture inter-application communications to link partial sensitive data flows from static analysis. This approach is faster than using only dynamic analysis. In this study, we use hooking technology to conduct dynamic analysis phase emulator modification. The experimental results show that the proposed system can detect most sensitive data leakages for both our dataset and real-world applications.

Currently, websites are widely used in various fields, including government. However, government websites are often the target of hacking. The XY Regency Communication and Information Agency experienced a website breach in 2023, which affected one of its subdomains, e-office.xy-regency.go.id, which stored employee data. This hack resulted in the leakage of sensitive data, user account takeovers, service disruptions, and a decline in public trust. The purpose of this study was to identify and test the vulnerabilities of the e-office.xy-regency.go.id website using the NIST SP 800-115 method, which includes the planning, discovery, attack, and reporting phases. Vulnerabilities were classified using the Common Vulnerability Scoring System (CVSS) and the OWASP Top 10 standard. This study identified two high-level vulnerabilities, namely Cross-Site Scripting (XSS) with a CVSS score of 8.2, classified as High severity, and Cross-Site Request Forgery (CSRF) with a CVSS score of 9.3, classified as Critical severity. These vulnerabilities could allow hackers to execute malicious scripts and manipulate users without their knowledge. Recommendations include implementing input validation on text boxes by limiting characters to letters or combinations of letters and numbers, and ensuring that all forms and endpoints that handle sensitive data are protected with unique and unpredictable CSRF tokens. Future research should focus on analyzing domain vulnerabilities, identifying the origin and potential of attacks, and developing effective protection and recovery strategies.

S. Saisree and V. Balaji With the growth of internet technologies data handling and sharing is inevitable and this is achieved by cloud computing. When a data outsourcer distributes sensitive data to the set of their subscribed and authentic agents, it is always possible that one of the authentic agents may leak the data to an unauthentic third party, this causes the sensitive data leak.

Embedded system security is often compromised when "trusted" software is subverted to result in unintended behavior, such as leakage of sensitive data or execution of malicious code. Several countermeasures have been proposed in the literature to counteract these intrusions. A common underlying theme in most of them is to define security policies at the system level in an application-independent manner and check for security violations either statically or at run time. In this paper, we present a methodology that addresses this issue from a different perspective. It defines correct execution as synonymous with the way the program was intended to run and employs a dedicated hardware monitor to detect and prevent unintended program behavior. Specifically, we extract properties of an embedded program through static program analysis and use them as the bases for enforcing permissible program behavior at run time. The processor architecture is augmented with a hardware monitor that observes the program's dynamic execution trace, checks whether it falls within the allowed program behavior, and flags any deviations from expected behavior to trigger appropriate response mechanisms. We present properties that capture permissible program behavior at different levels of granularity, namely inter-procedural control flow, intra-procedural control flow, and instruction-stream integrity. We outline a systematic methodology to design application-specific hardware monitors for any given embedded program. Hardware implementations using a commercial design flow, and cycle-accurate performance simulations indicate that the proposed technique can thwart several common software and physical attacks, facilitating secure program execution with minimal overheads

Although there has been much research on the leakage of sensitive data in Android applications, most of the existing research focus on how to detect the malware or adware that are intentionally collecting user privacy. There are not much research on analyzing the vulnerabilities of apps that may cause the leakage of privacy. In this paper, we present a vulnerability analyzing method which combines taint analysis and cryptography misuse detection. The four steps of this method are decompile, taint analysis, API call record, cryptography misuse analysis, all of which steps except taint analysis can be executed by the existing tools. We develop a prototype tool PW Exam to analysis how the passwords are handled and if the app is vulnerable to password leakage. Our experiment shows that a third of apps are vulnerable to leak the users' passwords.

Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP).This extension could introduce new security vulnerabilities.We present a systematic investigation of LLM vulnerabilities to hidden adversarial prompts through malicious font injection in external resources like webpages, where attackers manipulate code-to-glyph mapping to inject deceptive content which are invisible to users.We evaluate two critical attack scenarios:(1) "malicious content relay" and ( 2) "sensitive data leakage" through MCP-enabled tools.Our experiments reveal that indirect prompts with injected malicious font can bypass LLM safety mechanisms through external resources, achieving varying success rates based on data sensitivity and prompt design.Our research underscores the urgent need for enhanced security measures in LLM deployments when processing external content.

Data security has become an increasingly important topic as information stored and transmitted in electronic form has become the preferred method. In order to protect this information, developers need to follow, at minimum, some basic guidelines to secure this data from malicious attackers. However, security often takes a backseat while developing software and is either not implemented at all or is patched into the software at the end. Both are undesirable as it leave the software vulnerable to sensitive data leaks and lowers the overall quality of the application. These issues can be introduced by developers of any skill level. Existing static or dynamic analysis tool does not provide the functionality of interaction with user, which we believe can be an extremely valuable feature. We present Secure Sensitive Data (SSD) Eclipse IDE plug-in that can help bridge the gap in sensitive data leaks by aiding software developers interactively by pointing out issues in real-time and enforcing certain standards to protect sensitive data. Our SSD plugin enforces five best practices and standards that should at minimum keep data encrypted and proper handling of sensitive data. By continuously involving and reminding the developers of the security implications, SSD helps to mitigate security flaws in future software applications.

Owing to lack of authentication for client application (CA), traditional protection mechanism based on ARM TrustZone may lead to the sensitive data leakage within trusted execution environment (TEE). Furthermore, session resources will be occupied by malicious CA due to the design drawback for session mechanism between CA and trusted application (TA). Therefore, attackers can initiate a request to read the data stored in secure world or launch DoS attack by forging malicious CA. In order to address the authentication problems, this paper proposes a CA authentication scheme using ARM TrustZone. When CA establishes a session with trusted application, a CA authentication will be executed in TEE to prevent sensitive data from being accessed by malicious. At the same time, TA closes the session and releases occupied resources. The proposed authentication scheme is implemented on simulation platform built by QEMU and OP-TEE. The experimental results show that the proposed scheme can detect the content change of CA, avoid sensitive data leakage and prevent DoS attack.

Android is currently the most popular smartphone operating system in use, with its success attributed to the large number of applications available from the Google Play Store. However, these contain issues relating to the storage of the user’s sensitive data, including contacts, location, and the phone’s unique identifier (IMEI). Use of these applications therefore risks exfiltration of this data, including unauthorized tracking of users’ behavior and violation of their privacy. Sensitive data leaks are currently detected with taint analysis approaches. This paper addresses these issues by proposing a new static taint analysis framework specifically for Android platforms, termed “B-Droid”. B-Droid is based on static taint analysis using a large set of sources and sinks techniques, side by side with the fuzz testing concept, in order to detect privacy leaks, whether malicious or unintentional by analyses the behavior of Applications Under Test (AUTs). This has the potential to offer improved precision in comparison to earlier approaches. To ensure the quality of our analysis, we undertook an evaluation testing a variety of Android applications installed on a mobile after filtering according to the relevant permissions. We found that B-Droid efficiently detected five of the most prevalent commercial spyware applications on the market, as well as issuing an immediate warning to the user, so that they can decide not to continue with the AUTs. This paper provides a detailed analysis of this method, along with its implementation and results.

Wind energy is a fast-growing renewable energy but faces blade icing. Data-driven methods provide talented solutions for blade icing detection, but a considerable amount of Internet of Things data needs to be collected to a central server, which may lead to the leakage of sensitive business data. To address this limitation, this article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BLADE</i> , a Blockchain-empowered imbalanced federated learning (FL) model for blade icing detection. With the help of the Blockchain, the conventional FL is improved without worrying about the failure of the single centralized server and boosts the privacy preserving. A validation mechanism is introduced into the Blockchain to enhance the defense against poisoning attacks. In addition, a novel imbalanced learning algorithm is integrated into BLADE to solve the class imbalance problem in the sensor data. BLADE is evaluated on ten wind turbines from two wind farms. The experimental results verify the effectiveness, superiority, and feasibility of the proposed BLADE.

Efficient privacy preserving device authentication in WBANs for industrial e-health applications