English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

A long-standing technique to interfere with theft of sensitive data by its intended users is permitting these insiders only remote access to the data via a thin client. Even allowing only remote access is inadequate, however, to counter an insider willing to reconstruct the data from the graphical output, in the limit by photographing the data on-screen and applying automatic character recognition to these photographs offline. In this paper we propose and evaluate a system, called Snowman, that accurately monitors the amount of sensitive data output to a client. To conduct this monitoring without slowing the interactive user session, leakage is concurrently tracked in a replica of the application execution. This, in turn, introduces a key technical challenge that Snowman solves, namely identically replicating execution of an unmodified Linux binary while also performing efficient multi-label taint-tracking on it. We show through empirical measurements with a word processor, a spreadsheet program, and a code editor that Snowman induces little overhead on interactive user sessions and easily differentiates data-access patterns induced by normal usage and sufficiently aggressive data theft with reasonable responsiveness.

Metering Graphical Data Leakage with Snowman