Smart TV Data Sharing for Malicious Device Detection
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Solution Overview
Problem
Smart TVs are vulnerable to malware, which can compromise user data and transmit confidential information to hackers, posing a risk to user privacy and security.
Innovation Solution
A method involving an electronic server apparatus that collects data elements from multiple smart TV devices, performs risk analysis to generate risk scores, and provides an ordered list ranking the devices based on their malicious risk, sharing this information with a central server to identify and prevent fraudulent activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If smart TV devices collect and process data locally, then user convenience and device functionality are improved, but security and vulnerability to malware increase
Solution Approach 1:
The patent introduces a central server as an intermediary between smart TV devices and the external environment. This server collects data from multiple devices, performs centralized risk analysis, and distributes security updates. The intermediary handles security-critical functions remotely, allowing devices to maintain local functionality while reducing their security exposure and vulnerability to malware attacks.
2Reliability
If smart TV devices share data with external servers, then security through collective intelligence is improved, but data privacy and exposure to attacks worsen
Solution Approach 1:
The patent extracts sensitive personal information from the data collection process. The system collects device-level data elements (hardware identifiers, software versions, usage patterns) that are necessary for security analysis but deliberately excludes personally identifiable information. This extraction approach maintains security benefits of data sharing while minimizing privacy risks.
Solution Approach 2:
The patent transforms raw device data into risk scores through parameter changes. Instead of sharing or processing raw user data, the system converts it into aggregated risk metrics and security indicators. This transformation changes the data from potentially privacy-sensitive information into security-relevant parameters that can be shared safely across the network.
3Measurement precision
If risk analysis is performed on multiple devices simultaneously, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary risk analysis continuously in the background before security incidents occur. The central server maintains ongoing risk assessments of all connected devices, updating risk scores as new data becomes available. This preliminary action ensures that when a security threat is detected, the system already has pre-computed risk information ready, eliminating the need for time-consuming analysis during critical response moments.
Solution Approach 2:
The patent implements partial risk analysis by focusing computational resources on devices that exhibit suspicious behavior or match known threat patterns. Instead of continuously analyzing all devices at maximum depth, the system applies intensive analysis only where needed based on preliminary indicators, reducing overall processing time while maintaining detection accuracy for high-risk cases.
Data Source
AI summary
A technique shares smart television data among subscribing organizations to provide security. The technique involves collecting, by an electronic server apparatus, data elements from multiple smart television devices. The technique further involves performing, by the electronic server apparatus, a set of risk analysis operations to generate risk scores corresponding to the multiple smart television devices. Each risk score (e.g., a numerical value) indicates an amount of risk (e.g., a probability) that a respective smart television device is malicious. The technique further involves providing, by the electronic server apparatus, an ordered list of the multiple smart television devices, the ordered list ranking the multiple television devices based on the risk scores. Information from the ordered list is well suited for use by an anti-fraud service in which subscriber organizations are informed of the information and use the information to identify and stop fraudulent activity in the future.