From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying

Safeguarding the Intellectual Property (IP) of data has become critically important as machine learning applications continue to proliferate, and their success heavily relies on the quality of training data. While various mechanisms exist to secure data during storage, transmission, and consumption, fewer studies have been developed to detect whether they are already leaked for model training without authorization. This issue is particularly challenging due to the absence of information and control over the training process conducted by potential attackers. In this paper, we concentrate on the domain of tabular data and introduce a novel methodology, Local Distribution Shifting Synthesis (\textsc{LDSS}), to detect leaked data that are used to train classification models. The core concept behind \textsc{LDSS} involves injecting a small volume of synthetic data--characterized by local shifts in class distribution--into the owner's dataset. This enables the effective identification of models trained on leaked data through model querying alone, as the synthetic data injection results in a pronounced disparity in the predictions of models trained on leaked and modified datasets. \textsc{LDSS} is \emph{model-oblivious} and hence compatible with a diverse range of classification models, such as Naive Bayes, Decision Tree, and Random Forest. We have conducted extensive experiments on seven types of classification models across five real-world datasets. The comprehensive results affirm the reliability, robustness, fidelity, security, and efficiency of \textsc{LDSS}.Comment: 13 pages, 11 figures, and 4 table

Academic writing for IT students

This textbook is intended for Master and PhD Information Technology students (B1-C1 level of English proficiency). The instructions of how to write a research paper in English and the relevant exercises are given. The peculiarities of each section of a paper are presented. The exercises are based on real science materials taken from peer-reviewed journals. The subject area covers a wide scope of different Information Technology domains

SciTech News Volume 71, No. 1 (2017)

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Developing an Algorithm for Securing the Biometric Data Template in the Database

This research article published by the International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019In the current technology advancement, biometric template provides a dependable solution to the problem of user verification in an identity control system. The template is saved in the database during the enrollment and compared with query information in the verification stage. Serious security and privacy concerns can arise, if raw, unprotected data template is saved in the database. An attacker can hack the template information in the database to gain illicit access. A novel approach of encryption-decryption algorithm utilizing a design pattern of Model View Template (MVT) is developed to secure the biometric data template. The model manages information logically, the view shows the visualization of the data, and the template addresses the data migration into pattern object. The established algorithm is based on the cryptographic module of the Fernet key instance. The Fernet keys are combined to generate a multiFernet key to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and securely preserved in the database. In the event where an attacker tries to access the biometric data template in the database, the system alerts the user and stops the attacker from unauthorized access, and cross-verify the impersonator based on the validation of the ownership. Thus, helps inform the users and the authority of, how secure the individual biometric data template is, and provided a high level of the security pertaining the individual data privac

Blockchain-enabled authentication platform for the protection of 3D printing intellectual property: a conceptual framework study

This is the final version. Available on open access from Taylor & Francis via the DOI in this record3D printing (3DP) has enjoyed rapid growth yet has also prompted ethical and social concerns. For example, the ability to print unethical objects and intellectual property (IP) infringement. This paper follows the dual-cycle information system design model and implements a multi-method to propose a blockchain-enabled digital platform solution to protect 3DP digital assets’ IP. It combines the advantages of patented watermarking technology and blockchain technology, including encryption, authentication, and transaction services of 3DP designs according to users’ needs. The platform may help promote the standardised development of the 3DP industry and the international digital assets’ IP protection process.Arts and Humanities Research Council (AHRC)Newton Fun