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On the (In)effectiveness of Mosaicing and Blurring as Tools for Document Redaction

Abstract

In many online communities, it is the norm to redact names and other sensitive text from posted screenshots. Sometimes solid bars are used; sometimes a blur or other image transform is used. We consider the effectiveness of two popular image transforms - mosaicing (also known as pixelization) and blurring - for redaction of text. Our main finding is that we can use a simple but powerful class of statistical models - so-called hidden Markov models (HMMs) - to recover both short and indefinitely long instances of redacted text. Our approach borrows on the success of HMMs for automatic speech recognition, where they are used to recover sequences of phonemes from utterances of speech. Here we use HMMs in an analogous way to recover sequences of characters from images of redacted text. We evaluate an implementation of our system against multiple typefaces, font sizes, grid sizes, pixel offsets, and levels of noise. We also decode numerous real-world examples of redacted text. We conclude that mosaicing and blurring, despite their widespread usage, are not viable approaches for text redaction.

Open access
Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning

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Open access
(Do Not) Track Me Sometimes: Users’ Contextual Preferences for Web Tracking

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Open access
An Automated Approach for Complementing Ad Blockers’ Blacklists

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Open access
Automated Experiments on Ad Privacy Settings
A Tale of Opacity, Choice, and Discrimination

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Open access
Social Engineering Attacks on Government Opponents: Target Perspectives

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Open access
Analyzing Remote Server Locations for Personal Data Transfers in Mobile Apps

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Open access
Portrait of a Privacy Invasion
Detecting Relationships Through Large-scale Photo Analysis

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Open access
Every Move You Make: Exploring Practical Issues in Smartphone Motion Sensor Fingerprinting and Countermeasures

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Open access
Individual versus Organizational Computer Security and Privacy Concerns in Journalism

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Open access