Probabilistic Blockchain Transaction Filtering for Illicit Content
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Solution Overview
Problem
Existing blockchain systems lack effective methods to identify and prevent transactions associated with illicit activities or to ensure the legitimacy of transactions, making them susceptible to criminal use and compromising user security.
Innovation Solution
Implementing probabilistic filters, such as Bloom or Cuckoo filters, to encode whitelisted or blacklisted data items, allowing nodes to determine transaction processing based on membership tests, thereby preventing or allowing transactions based on predefined lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain nodes process all transactions without filtering, then transaction processing completeness is maintained, but security against illicit content deteriorates
Solution Approach 1:
The system performs preliminary filtering of transactions before they are fully processed by blockchain nodes. Probabilistic filters are pre-computed and distributed to nodes, enabling them to quickly identify and reject transactions containing illicit content (such as illegal videos, images, or text) without performing complex analysis on every transaction, thus enhancing security while maintaining processing efficiency
Solution Approach 2:
A probabilistic filter acts as an intermediary between the transaction pool and blockchain nodes. This filter encodes sets of illicit content identifiers and enables nodes to determine whether a transaction contains prohibited content through efficient membership tests, serving as a mediating layer that improves security without requiring nodes to directly analyze all transaction contents
2Reliability
If probabilistic filters are used to filter transactions, then security against illicit content is improved, but processing speed deteriorates due to additional filtering steps
Solution Approach 1:
The system uses probabilistic filters that are computationally inexpensive and can be quickly updated or replaced. These filters are designed to be lightweight data structures that enable fast membership tests, allowing nodes to perform filtering operations with minimal computational overhead, thus maintaining high transaction processing speeds while improving security
Solution Approach 2:
The system adjusts parameters of the probabilistic filter (such as false positive rate and filter size) to optimize the balance between security and processing speed. By tuning these parameters, the system can achieve high security coverage while minimizing the impact on transaction processing throughput, ensuring that the filtering mechanism does not become a bottleneck
3Measurement precision
If comprehensive transaction filtering is implemented, then detection precision of illicit content is improved, but data storage requirements worsen
Solution Approach 1:
The system extracts only the essential identifying features of illicit content into the probabilistic filter, rather than storing complete copies of all illicit materials. The filter encodes hashed representations or key identifiers of prohibited content, enabling detection without requiring nodes to store large volumes of actual illicit data, thus achieving high detection precision with minimal storage requirements
Data Source
AI summary
A computer-implemented method of processing a blockchain transaction, wherein the method is performed by a receiving party and comprises: obtaining one or more probabilistic filters, wherein each probabilistic filter encodes one of i) one or more sets of whitelisted data items, or ii) one or more sets of blacklisted data items; obtaining a blockchain transaction, wherein the obtained blockchain transaction is associated with a candidate data item corresponding to i) one of the one or more sets of whitelisted data items, or ii) one on the one or more sets of blacklisted data items; and determining whether to process the obtained blockchain transaction based on whether the candidate data item is present in at least one of the one or more probabilistic filters.