App Launch Warnings Using Probabilistic Asset Flagging
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Solution Overview
Problem
Existing computing devices lack effective mechanisms to identify and warn users about potentially problematic software applications, allowing malicious or faulty applications to be installed and executed without adequate user notification.
Innovation Solution
Implementing a system where computing devices maintain a probabilistic data structure, such as a Bloom Filter, to flag problematic software application assets, interface with a management entity for definitive identification, and display warnings or remedial information based on an informational package.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional antivirus software is used to scan applications, then security protection is provided, but system performance deteriorates due to resource consumption and user experience degradation
Solution Approach 1:
The system performs preliminary actions by scanning and analyzing application assets before installation occurs. The probabilistic data structure pre-flags potentially problematic assets, and the management entity pre-generates informational packages with warnings. This allows security checks to be completed prior to installation, eliminating the need for continuous scanning during operation and thus maintaining system performance while ensuring security protection.
Solution Approach 2:
Instead of requiring real-time scanning of every application asset, the system creates a probabilistic data structure that stores flagged assets as a copy/reference mechanism. When an application is installed, the system checks against this pre-built structure rather than performing comprehensive scanning, significantly reducing computational resources needed during operation while maintaining security awareness.
2Measurement precision
If comprehensive application scanning is performed, then detection accuracy is improved, but installation time increases
Solution Approach 1:
The system performs comprehensive scanning and asset flagging as a preliminary action before installation time. The probabilistic data structure is built in advance by scanning the asset database and marking potentially problematic applications. During installation, the system only needs to check against this pre-computed structure rather than performing full scanning, thus maintaining high detection accuracy while minimizing installation time.
Solution Approach 2:
The system extracts the essential security assessment information from comprehensive scanning by using the probabilistic data structure to identify and store only the flagged problematic assets. This extraction allows the system to retain detection accuracy for problematic applications while avoiding the time-consuming full scanning process during installation, as only checks against the extracted flagged list are required.
3Loss of information
If user warnings are displayed for all flagged applications, then user awareness is improved, but user experience deteriorates due to frequent interruptions
Solution Approach 1:
The system applies local quality by providing targeted warnings only for specific problematic assets identified through the probabilistic data structure, rather than blanket warnings for all applications. The management entity generates informational packages with warnings only when necessary, and the user interface displays these warnings in a localized manner associated with the specific application launch, maintaining user awareness for problematic applications while avoiding unnecessary interruptions for safe applications.
Solution Approach 2:
The system implements feedback by using the probabilistic data structure to provide informed warnings to users about potentially problematic applications. The management entity receives feedback about flagged assets and generates appropriate warnings, which are then displayed to users. This feedback mechanism ensures users are informed about actual risks while avoiding unnecessary warnings for safe applications, thus balancing user awareness with positive user experience.
Data Source
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AI summary
This Application sets forth techniques for displaying warnings when potentially problematic software applications are launched on computing devices. According to some embodiments, a computing device can implement the steps of (1) maintaining a probabilistic data structure that is based on a plurality of software application assets that have been flagged as problematic, (2) installing a software application that is comprised of at least one software application asset, (3) identifying, by interfacing with the probabilistic data structure and a management entity, that the at least one software application asset has in fact been flagged as problematic, (4) assigning, to the software application, an informational package that is received from the management entity and that pertains to the at least one software application asset, and (5) displaying, in association with launching the software application, a user interface that is derived, at least in part, from the informational package.