NDLI: Opportunities for private and secure machine learning

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Open problems in the security of learning

Opportunities for private and secure machine learning

Robust content-driven reputation

Adaptive distributed mechanism against flooding network attacks based on machine learning

Cyber security and artificial intelligence: from fixing the plumbing to smart water

Malware detection using adaptive data compression

User-controllable learning of security and privacy policies

Cognitive security for personal devices

A data mining approach for analysis of worm activity through automatic signature generation

Automatic feature selection for anomaly detection

Opportunities for private and secure machine learning

Content Provider	ACM Digital Library
Author	Clifton, Christopher W.
Abstract	While the interplay of Artificial Intelligence and Security covers a wide variety of topics, the 2008 AISec program largely focuses on use of artificial intelligence techniques to aid with traditional security concerns: intrusion detection, security policy management, malware detection, etc. This talk will address the flip side of the issue: Using machine learning on sensitive data. The privacy-preserving data mining literature provides numerous solutions to machine learning on sensitive data, while protecting the data from disclosure. Unfortunately, privacy has yet to provide the economic incentives for commercial development of this technology. This talk will survey this work (and open challenges) in light of problems that may have greater incentives for development: collaborative machine learning by parties that do not fully trust each other. Opportunities include job brokerage (assigning jobs in ways that most efficiently utilize resources of competing companies), supply chain optimization, inter-agency data sharing, etc. Techniques similar to those in privacy-preserving data mining can enable such applications without the degree of information disclosure and trust currently required, providing a business model for development of the technology (and as a by-product, reducing the number of trusted systems that need to be secured.).
Starting Page	31
Ending Page	32
Page Count	2
File Format	PDF
ISBN	9781605582917
DOI	10.1145/1456377.1456385
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Privacy Security Artificial intelligence
Content Type	Text
Resource Type	Article

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