Ownership and Tamper Detection of Relational Data: Framework, Techniques and Security Analysis

Khanduja, Vidhi; Chakraverty, Shampa; Verma, Om Prakash

doi:10.15579/gcsr.vol5.ch2

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…Other approaches based on virtual primary keys are proposed in [23]- [27]. Approaches in [23], [24] are similar to the M-Scheme. Two attributes having hash values near zero are considered.…”

Section: B: Virtual Primary Key Basedmentioning

confidence: 99%

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Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Rani

Halder

2022

IEEE Access

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Digital watermarking is considered one of the most promising techniques to verify the authenticity and integrity of digital data. It is used for a wide range of applications, e.g., copyright protection, tamper detection, traitor tracing, maintaining the integrity of data, etc. In the past two decades, a wide range of algorithms for relational database watermarking has been proposed. Even though a number of surveys exist in the literature, they are unable to provide insightful guidance to choose the right watermarking technique for a given application. In this paper, we provide an exhaustive empirical study and thorough comparative analysis of various relational database watermarking techniques in the literature. Our work is different from the existing survey papers as we consider both distortion-based and distortion-free techniques along with a rigorous experimental analysis demonstrating a detailed comparison on robustness, data usability, and computational cost with considerable empirical evidence. INDEX TERMSDigital watermarking, relational database, empirical study, robustness, data usability. RAJU HALDER (Member, IEEE) received the Ph.D. degree from Università Ca´Foscari, Italy, in 2012. He is currently an Associate Professor with the Department of Computer Science and Engineering, IIT Patna. Before joining IIT Patna, he worked as a Postdoctoral Researcher with Macquarie University, Australia. He worked with the Robotics Team at HASLab (University of Minho), Portugal, in 2016. Prior to his Ph.D. degree, he had also worked as an Associate System Engineer at IBM India Private Ltd., from 2007 to 2008. His research interests include formal methods, blockchain technology, program analysis and verification, and data privacy and security.

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Section: B: Virtual Primary Key Basedmentioning

confidence: 99%

“…In [23], the textual attributes are considered. The VPK is generated based on two numeric attributes in [24]. Different attributes are selected each time in [25].…”

Section: B: Virtual Primary Key Basedmentioning

confidence: 99%

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Rani

Halder

2022

IEEE Access

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“…Several recent surveys have provided detailed discussion on relational database watermarking [1][2][3][4][5]. Robust reversible watermarking has also been investigated in many recent works [10][11][12][13].…”

Section: Previous Workmentioning

confidence: 99%

“…at is, they do not allow original data recovering from the watermarked relation [1][2][3][4]. erefore, although some distortion-free schemes exist in the literature [1][2][3][4][5][6][7][8][9], there is a need to develop a new generation of watermarking techniques that could efficiently address the abovementioned issues. To achieve that, reversible or lossless digital watermarking has been extended from multimedia assets to relational data to allow original data recovery at watermark detection.…”

Section: Introductionmentioning

confidence: 99%

Reversible Fragile Watermarking Scheme for Relational Database Based on Prediction-Error Expansion

Hamadou

Camara

Hassane

et al. 2020

Mathematical Problems in Engineering

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The protection of database systems content using digital watermarking is nowadays an emerging research direction in information security. In the literature, many solutions have been proposed either for copyright protection and ownership proofing or integrity checking and tamper localization. Nevertheless, most of them are distortion embedding based as they introduce permanent errors into the cover data during the encoding process, which inevitably affect data quality and usability. Since such distortions are not tolerated in many applications, including banking, medical, and military data, reversible watermarking, primarily designed for multimedia content, has been extended to relational databases. In this article, we propose a novel prediction-error expansion based on reversible watermarking strategy, which not only detects and localizes malicious modifications but also recovers back the original data at watermark detection. The effectiveness of the proposed method is proved through rigorous theoretical analysis and detailed experiments.

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“…Such techniques use a watermark string and create partitions using primary key and the binary string of watermark. The tuples in the partitions are then rearranged by comparison of their monotonicity, and relies on order of the tuples in the database to verify status of the database [17].…”

Section: Prior Workmentioning

confidence: 99%

Dynamic Watermark Injection in NoSQL Databases

Khanduja¹

2017

JCSAIT

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Open Access Research articledata. Further, dynamic data analysis of such huge data requires Hadoop like map-reduce stacks [3]. For example, Netflix, an online video viewing subscription based platform relies on customer's viewing preferences to recommend better titles which are more relevant [4]. Face book tracks user interactions with news feed items, friends and pages, photos, text posts etc. to provide better features on its social networking platform.Data in modern web services and applications thus plays really important role. This creates necessity of Technologically Protective Measures (TPM) to protect the company data related assets [5]. One technique popular in this field is of Watermarking. The core idea is to embed a watermark into tuples of database. Watermark is usually based on some secret string chosen by owner and hashing functions. However, static watermarking works on snapshots of databases and often uses techniques that are slow and can't be scaled for big data easily. Big data is dynamic in the sense that the rate of change in it is too high, and the size is too huge, which makes static watermarking on snapshots nearly impossible. This poses a challenge to traditional watermarking techniques in order to implement TPM on Big Data.The dynamically increasing NoSQL databases with irregularities in schema require parallelizable and atomic watermarking techniques that deal with following problems:(a) The technique shouldn't rely on schema specific details as NoSQL databases tend to change very often and with document store systems like MongoDB, CouchDB, RethinkDB etc, schema is flexible and different documents can have different structure.(b) Technique shouldn't work on a snapshot of database but should work dynamically as and when data comes. This would mean the implementation should have minimum computational overhead to be worth.(c) Technique should exploit new features of NoSQL databases like support for new data types and sharded/ distributed databases that can't support inter tuple dependency for watermark embedding.Considering these issues in mind, a technique is proposed to protect integrity of NoSQL databases. In this work, the emphasis is laid on the challenges faced while designing the technique and AbstractWith the advent of gathering real time information, the need of Not Only SQL (NoSQL) databases has skyrocketed. No prior work is done on watermarked protection of such Schema-less databases. Traditional watermarking techniques cannot be applied as NoSQL databases are dynamically increasing and often have irregularities in schema. In this paper, a new perspective of embedding watermark into such databases is proposed. Watermark that acts as a signature is securely prepared for each tuple individually. The proposal deals with the issues by leveraging the flexible schema features provided by NoSQL database. A new attribute is dynamically injected into the tuple before inserting or updating it in database. Experimental results and analyses demonstrate that with even the slightest modifica...

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Ownership and Tamper Detection of Relational Data: Framework, Techniques and Security Analysis

Cited by 4 publications

References 24 publications

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Reversible Fragile Watermarking Scheme for Relational Database Based on Prediction-Error Expansion

Dynamic Watermark Injection in NoSQL Databases

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