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Ma, Ao; Dragga, Chris; Arpaci-Dusseau, Andrea C.; Arpaci-Dusseau, Remzi H.; McKusick, Marshall

doi:10.1145/2560011

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…This significant metadata may change during its collection and migration process. When this happens, it results in a metadata authentication problem [26], and sometimes it could cause metadata damage [27]. This is a major challenge, especially during the e-discovery procedure for data or information governance.…”

Section: Use Of Fog Metadata For Authenticationmentioning

confidence: 99%

Digital Forensics Challenges in Cyberspace: Overcoming Legitimacy and Privacy Issues Through Modularisation

Moses Ashawa,

Ali Mansour,

Jackie Riley

et al. 2023

Cloud Computing and Data Science

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The significance of the cloud environment is growing in the current digital world. It provides several advantages, such as reduced expenses, the ability to adjust to different needs, adaptability and enhanced cooperation. The field of digital forensic investigations has encountered substantial difficulties in reconciling the requirement for efficient data analysis with the increasing apprehensions regarding privacy in recent times. As investigators analyse digital evidence to unearth crucial information, they must also traverse an intricate network of privacy rules and regulations. Given the increasing prevalence of remote work and the necessity for businesses to be adaptable and quick to react to shifting market circumstances, the cloud infrastructure has become a crucial asset for organisations of various scales. Although the cloud offers benefits such as scalability, flexibility and enhanced collaboration, it presents difficulties in digital forensic investigations regarding data protection, ownership and jurisdictional boundaries. These concerns are becoming increasingly significant as more data is kept in the cloud. In this paper, we present three major challenges that are faced during cloud-based forensics investigation. We analyse the extent to which different data formats increase complexity in forensics investigations in cyberspace. This paper analyses three core challenges facing digital forensics in the cloud environment: legitimacy, complexity and an increase in data volume, looking at the implications these have on data liable for legal issues in court. These challenges contribute to the backlog in digital forensics investigations due to a lack of modularisation of the procedures. To address these concerns, modularisation model is proposed to offer a way to integrate traditional processing functions while ensuring strict adherence to privacy protocols. To overcome these challenges, we propose modularisation as a strategy for improving the future of digital forensic research's operational efficiency, overcoming the identified challenges faced during cloud-based investigations and demonstrating how organisations can mitigate potential risks associated with storing sensitive information in the cloud.

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Section: Use Of Fog Metadata For Authenticationmentioning

confidence: 99%

Digital Forensics Challenges in Cyberspace: Overcoming Legitimacy and Privacy Issues Through Modularisation

Moses Ashawa,

Ali Mansour,

Jackie Riley

et al. 2023

Cloud Computing and Data Science

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“…Ensuring the correctness of the transferred data is one of the primary concerns of data transfer frameworks. Many studies have been performed on the design and implementation of data integrity verification and their optimization in data storage [34,36,37], cloud storage [38][39][40][41][42], file systems [32][33][34][35]43], databases [32,44,45], and data transfer systems [18,19,46,47].…”

Section: Related Workmentioning

confidence: 99%

TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems

2022

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Computational science simulations produce huge volumes of data for scientific research organizations. Often, this data is shared by data centers distributed geographically for storage and analysis. Data corruption in the end-to-end route of data transmission is one of the major challenges in distributing the data geographically. End-to-end integrity verification is therefore critical for transmitting such data across data centers effectively. Although several data integrity techniques currently exist, most have a significant negative influence on the data transmission rate as well as the storage overhead. Therefore, existing data integrity techniques are not viable solutions in high performance computing environments where it is very common to transfer huge volumes of data across data centers. In this study, we propose a two-phase Bloom-filter-based end-to-end data integrity verification framework for object-based big data transfer systems. The proposed solution effectively handles data integrity errors by reducing the memory and storage overhead and minimizing the impact on the overall data transmission rate. We investigated the memory, storage, and data transfer rate overheads of the proposed data integrity verification framework on the overall data transfer performance. The experimental findings showed that the suggested framework had 5% and 10% overhead on the total data transmission rate and on the total memory usage, respectively. However, we observed significant savings in terms of storage requirements, when compared with state-of-the-art solutions.

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“…Traditional digital data storage systems offer various solutions for data recovery in the event of medium failure. For hard disk drives, this typically involves the use of software tools for error prediction [13] and (file system ) recovery [14,15] as well as near-hardware procedures to retrieve otherwise inaccessible user data [16]. While recovering data from a failed hard disk drive is generally possible, any error exceeding the chosen maximum redundancy for a DNA storage system leads to irreversible data loss.…”

Section: Introductionmentioning

confidence: 99%

Data recovery methods for DNA storage based on fountain codes

Schwarz,

Freisleben

2024

Preprint

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Today’s digital data storage systems typically offer advanced data recovery solutions for the problem of catastrophic data loss. For conventional hard disk drives, several data recovery mechanisms are available, ranging from software-based disk sector or file analysis to physicallevel data retrieval methods. In contrast, DNA-based data storage exclusively relies on the inherent error correction properties of the encoding methods employed to encode digital data into strands of DNA. In this article, we propose a novel approach to provide data recovery in DNA storage systems. In particular, we present a novel method to automatically reconstruct original data from corrupted data stored and encoded in DNA using fountain codes. Our method exploits the intrinsic relationships between processed packets encoded with fountain codes to identify and rectify errors. Furthermore, we present file type-specific and contentbased error detection and correction methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the stored data can effectively recover information in a partially corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate the feasibility of our approach, we introduce DR4DNA, a software toolkit that contains all methods presented in this work. Compared to conventional DNA error correction schemes, our approach introduces a fallback recovery approach similar to data recovery services for hard drives. We evaluate DR4DNA using both in-silico and in-vitro experiments.

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Ffsck

Cited by 17 publications

References 15 publications

Digital Forensics Challenges in Cyberspace: Overcoming Legitimacy and Privacy Issues Through Modularisation

Digital Forensics Challenges in Cyberspace: Overcoming Legitimacy and Privacy Issues Through Modularisation

TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems

Data recovery methods for DNA storage based on fountain codes

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