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  1. Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics (IWSPA '16)
  2. How Can We Enable Privacy in an Age of Big Data Analytics?
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Evaluation of Android Malware Detection Based on System Calls
How Can We Enable Privacy in an Age of Big Data Analytics?
Data Driven Data Center Network Security
Security Analytics in the Context of Adversarial Machine Learning
Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection
Differential Privacy for Collaborative Filtering Recommender Algorithm
Static Analysis of Malicious Java Applets
Countering Phishing from Brands' Vantage Point
Detecting Advanced Persistent Threats using Fractal Dimension based Machine Learning Classification
Clone or Relative?: Understanding the Origins of Similar Android Apps
Towards Automatic Identification of JavaScript-oriented Machine-Based Tracking
Malware Detection Using Dynamic Birthmarks

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How Can We Enable Privacy in an Age of Big Data Analytics?

Content Provider ACM Digital Library
Author Landwehr, Carl E.
Abstract Even though some seem to think privacy is dead, we are all still wearing clothes, as Bruce Schneier observed at a recent conference on surveillance[1]. Yet big data and big data analytics are leaving some of us feeling a bit more naked than before. This talk will provide some personal observations on privacy today and then outline some research areas where progress is needed to enable society to gain the benefits of analyzing large datasets without giving up more privacy than necessary. Not since the early 1970s, when computing pioneer Willis Ware chaired the committee that produced the initial Fair Information Practice Principles [2] has privacy been so much in the U.S. public eye. Snowden's revelations, as well as a growing awareness that merely living our lives seems to generate an expanding "digital exhaust." Have triggered many workshops and meetings. A national strategy for privacy research is in preparation by a Federal interagency group. The ability to analyze large datasets rapidly and to extract commercially useful insights from them is spawning new industries. Must this industrial growth come at the cost of substantial privacy intrusions?.
Starting Page 47
Ending Page 47
Page Count 1
File Format PDF
ISBN 9781450340779
DOI 10.1145/2875475.2875489
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-03-11
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Fair information practice principles Privacy Surveillance
Content Type Text
Resource Type Article
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