Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. (2015)

• Record Type: Journal Article
• Authors: Ateniese, Giuseppe; Mancini, Luigi V.; Spognardi, Angelo; Villani, Antonio; Vitali, Domenico; Felici, Giovanni
• Is Part Of: International journal of security and networks. Volume 10:Number 3(2015)
• Page Start: 137
• Page End: 150
• Publication Date: 2015
• Subjects: machine learning classifiers -- information leakages -- attack methodology -- unauthorised access -- trade secrets -- intellectual property rights -- IPR -- security -- meta-classifiers -- classifier hacking -- training sets -- hacking attacks; Computer networks -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1747-8405

Abstract

Machine-learning (ML) enables computers to learn how to recognise patterns, make unintended decisions, or react to a dynamic environment. The effectiveness of trained machines varies because of more suitable ML algorithms or because superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. In this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. Such information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights.