Spam Score Noise Injection for Search Algorithm Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines face difficulties in effectively identifying and removing fraudulent business listings due to spammers reverse-engineering ranking algorithms, leading to a challenge in maintaining accurate search results.
Innovation Solution
Introducing noise to spam scores to make them non-deterministic, making it harder for spammers to reverse-engineer the detection algorithms, with thresholds determining the legitimacy and penalties for listings, such as removal or demotion from search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines use ranking algorithms to identify and exclude fake business spam listings, then the accuracy of search results is improved, but spammers can reverse engineer the algorithms to circumvent detection
Solution Approach 1:
The patent applies parameter changes by introducing noise to spam scores, transforming the detection system from a deterministic to a probabilistic approach. By adding random noise to the spam score calculation, the system makes it difficult for spammers to reverse engineer the algorithm while maintaining effective spam detection. The noise parameter is carefully controlled to ensure legitimate listings are not adversely affected.
2Difficulty of detecting and measuring
If noise is added to spam scores to prevent reverse engineering, then algorithm security is improved, but the ranking of legitimate listings may be affected
Solution Approach 1:
The patent applies local quality by differentiating the treatment of spam scores based on their magnitude. Small noise amounts are added to spam scores of legitimate listings to prevent reverse engineering, while larger noise amounts are applied to potentially fraudulent listings. This localized approach ensures that noise only affects the detection process when necessary, preserving the ranking integrity of legitimate businesses.
Solution Approach 2:
The patent applies partial action by selectively applying noise to spam scores based on threshold criteria. Not all spam scores receive the same noise treatment - only those above certain thresholds receive noise addition. This partial application minimizes the impact on legitimate listings while still providing sufficient obfuscation to prevent algorithm reverse engineering by spammers.
3Difficulty of detecting and measuring
If spam scores are made non-deterministic through noise addition, then spammer circumvention becomes difficult, but the system complexity increases
Solution Approach 1:
The patent applies the intermediary principle by introducing noise as a mediating element between the spam detection algorithm and the spam score output. The noise acts as a buffer that obscures the deterministic relationship between algorithm inputs and outputs, making reverse engineering difficult without requiring fundamental changes to the underlying detection algorithm. This intermediary approach adds complexity only where necessary - in the noise addition layer - while preserving the core detection logic.
Data Source
AI summary
A spam score is assigned to a business listing when the listing is received at a search entity. A noise function is added to the spam score such that the spam score is varied. In the event that the spam score is greater than a first threshold, the listing is identified as fraudulent and the listing is not included in (or is removed from) the group of searchable business listings. In the event that the spam score is greater than a second threshold that is less than the first threshold, the listing may be flagged for inspection. The addition of the noise to the spam scores prevents potential spammers from reverse engineering the spam detecting algorithm such that more listings that are submitted to the search entity may be identified as fraudulent and not included in the group of searchable listings.