An improved data leakage detection system in a cloud computing environment

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.

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.

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.

Data leakage is a critical threat to app developers because apps use and reserve a variety of sensitive data collected from users. While using third-party SDK is a common way to build apps, some accessed data may not be perceived by app developers. Therefore, the use of third-party SDKs may cause data leakage. This work proposes a platform named "WaRning-Awareness Platform" (WRAP). WRAP records profiles of SDKs, and reveals sensitive data accessed by SDKs. To demonstrate the data leakage caused by third-party SDKs, this work conducts a case study with various categories of Android apps. The result of our case study indicates that if app developers do not pay attention to the accessed data, the data would be leaked.

The data leaks problem is a security key issue in the worldwide connection, communication and interoperability functions among the huge number of mobile devices, especially for apps where sensitive data are exchanged. In order to tackle this dangerous problem, an innovative and powerful tool, named Android JADAL (JAva DAta Leak), has been developed based on hybrid approach which combines both static and dynamic code analysis techniques. This tool has been validated successfully by means of significant tests carried out on sensitive data leak applications. During the test, the user receives notification when sensible data is caught during the execution of apps. The design and testing activities have been conducted in cooperation with the Mathematics and Computer Science Department of Catania University.

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

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.

Despite the availability of data leak detection and prevention tools, there are currently a growing number of confidential data leaks through the fault of insiders. One of the possible data leak channels is encrypted or compressed data transfer, because the existing data leak detection tools use content data analysis methods. This article presents an algorithm of detecting encrypted and compressed data that is based on the statistical model of pseudo-random sequences and allows detecting encrypted and compressed data to an accuracy of 0.99.

Exchange of information is done in every organization. Information can be confidential and non-confidential. There are several channels through which exchange of data is done. During this process of exchange, data gets leaked. Enterprises are facing a lot of issues with data leakage and also towards recognizing the leakers. It is a very important need for the organization to exchange confidential information securely and easily. The main goal is to create the data leakage prevention and detection model which detects when the allocator’s sensitive information has been disclosed by operators and also to identify the leaker that leaked the data. Leakage of confidential information leads to a great loss to the organization. In addition to financial loss, an organization’s reputation is ruined and facing its identity stolen. These losses take a lot of time to recover. In this paper, it will focus on research papers on various techniques, which consists of classification strategies to identify sensitive and non-sensitive data, distribution strategy to monitor data, and encryption technique used.

In today's digital landscape, ensuring the security of sensitive data and protecting against network intrusions are critical challenges. This research project develops and evaluates a novel context-aware adaptive encryption system that integrates sensitive data detection, network intrusion detection, and dynamic encryption techniques to enhance data security. The proposed system employs deep learning models to identify sensitive information and machine learning algorithms to monitor network activity for potential intrusions. Upon detecting sensitive data or a security threat, the system automatically applies encryption with adjustable strength based on the context, increasing protection in high-risk situations. This approach minimizes unnecessary overhead in low-risk scenarios while maintaining robust security measures. Through simulations using real-world data, the system's effectiveness in accurately detecting sensitive information and network intrusions, as well as its capability to adapt encryption dynamically, is evaluated. The results demonstrate the potential of combining machine learning with adaptive security measures to create a responsive and efficient data protection system.

Sensitive data is leaked in software firm, government sectors and also from the web. To prevent this leakage, data allocation strategies are used. Data allocation Strategy improves the probability of identifying leakages. The current in leakage detection is handled by watermarking. Watermarks can be useful in some of the cases, but it again, modifies of the original data. If the recipient is malicious watermarks can be destroyed. After giving a set of data to agents, the distributor saw some of the same data in an unauthorized place. At this point the distributor came to know that the data is leaked and came from one or more agents. If the distributor got the enough evidence that an agent leaked data, he may initiate legal proceedings. The data allocation techniques are used to identify the leaking agent. The model is used to find the probability of leaking agent. The ECDSA algorithm is used to prevent the data from untrusted party. The Algorithm used is Sample data request and explicit data request. By implementing guilt and ECDSA techniques the distributor can identify the leaking agents and to whom the data leaks.

Federated Learning (FL) has emerged as a suitable option for collaborative, user-centric machine learning while protecting sensitive user data. FL differs significantly from traditional centralized frameworks that collect raw data on a central server. FL focuses on collecting model updates generated locally on edge devices. With traditional FL approaches, data must be downloaded to a central server for model training. In contrast, training occurs on the edge and only model updates are sent. This decentralized framework, unlike traditional FL approaches, is ideal for ubiquitous applications—systems woven seamlessly into everyday surroundings like smart homes, advanced mobile devices, and IoT systems—where sensitive data is constantly and automatically generated. In these systems, user privacy is paramount, and advanced methods must be implemented to ensure privacy and system efficacy. This paper discusses the integration of FL into ubiquitous environments, addressing the relevance, advantages, and disadvantages of this approach. I’d like to focus on privacy-preserving methods, but their preservation against data leakage and adversary attacks adds significant value. There are case studies to show the challenges encountered, lessons learned, and the use of FL within privacy-centric ubiquitous systems. In closing this paper, I summarize the key points and underscore the transformational potential of FL for privacy-sensitive, innovative systems. I also promote further research in this area to support its practical relevance. This paper demonstrates that among the many approaches to privacy within pervasive computing, FL is the only one that is practical and scalable.

In this paper, we describe the feasibility and practical study of the recently proposed idea for data leak prevention (DLP) based on end-point policy enforcement. The most reassuring way to prevent sensitive data leak is to thwart sensitive data export before it has a chance to occur. Using a System Call Interception (SCI) technique we investigate the possibility of automatically detecting and amending a non-desired, policy breaching behavior at the "intention" stage: as the corresponding system call is called by an application, but before the action has been accomplished. The SCI method is especially valuable for "black box" applications, for which source code is not available. In our system, we catalog the system calls involved in the DLP events, and reduce our SCI to the minimum necessary set of system calls associated with the sensitive, DLP-requiring tasks. We describe the system behavior for several different applications that we have studied to date.

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.

Data leakage poses a significant threat to organizations, exposing sensitive information to unauthorized parties and potentially resulting in severe consequences. Detecting and preventing data leakage is paramount for safeguarding organizational assets and maintaining trust with stakeholders. This paper provides a comprehensive review of techniques and approaches for data leakage detection, focusing on both traditional methods and recent advancements in the field. We examine the various types of data leakage, including intentional and unintentional breaches, and discuss the challenges associated with detecting such incidents. Furthermore, we explore the role of machine learning, encryption, and anomaly detection in mitigating data leakage risks. By synthesizing existing research and identifying areas for future investigation, this review aims to contribute to the development of effective strategies for detecting and mitigating data leakage threats.