Secure Multi-Party Sum Protocol Using Nested Encryption
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Solution Overview
Problem
Existing multi-party data mining protocols are insecure, particularly when more than two parties collude, and rely on trusted mediators who can breach privacy, making it difficult to scale for big data analytics due to computational complexities.
Innovation Solution
A secure-sum protocol using a non-trusted mediator where each party encrypts and anonymizes data using public and private keys, allowing the mediator to compute a global sum without accessing raw data, thus minimizing the risk of data breach and maintaining privacy even with up to N−2 parties colluding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If round-robin protocols based on randomization are used for multi-party data mining, then data privacy can be preserved to some extent, but the protocols become insecure when more than two parties collude and tend to leak information
Solution Approach 1:
The patent introduces a non-trusted mediator that facilitates secure multi-party computation by collecting encrypted data from all parties, performing computations on encrypted values, and distributing results. This mediator approach eliminates the need for complex peer-to-peer secure protocols while maintaining security against collusion, as the mediator never receives plaintext data and parties cannot directly exchange sensitive information.
Solution Approach 2:
The patent segments the computation process into distinct phases: data submission phase where parties send encrypted values to the mediator, computation phase where the mediator processes encrypted data, and result distribution phase where encrypted results are returned to parties. This segmentation allows each phase to be optimized independently and simplifies the overall protocol design while maintaining security properties.
2Reliability
If peer-to-peer secure protocols are used for multi-party computation, then data privacy can be preserved, but computational complexity increases making it difficult to scale to big data analytics
Solution Approach 1:
The non-trusted mediator centralizes the computational burden, performing all secure computations on encrypted data received from parties. This eliminates the need for each party to engage in complex cryptographic operations with every other party, reducing computational complexity from O(N^2) peer-to-peer interactions to O(N) mediator-collected interactions, thereby enabling scaling to big data analytics.
Solution Approach 2:
The patent uses encryption to create copies of data that can be processed by the mediator without revealing sensitive information. The mediator works with encrypted copies of the parties' data, performing computations that would be infeasible or insecure in plaintext, thus maintaining both security and computational efficiency.
3Ease of operation
If a third-party trusted mediator is used for analytic data and result sharing, then communication is simplified, but the third-party becomes a weak link in the security chain
Solution Approach 1:
Instead of trusting the mediator with plaintext data, the system inverts the trust model: the mediator is explicitly not trusted and is given only encrypted data. Security is achieved not by trusting the mediator's integrity but by ensuring the mediator cannot decrypt the data, thus eliminating the trust requirement while maintaining communication simplicity.
Solution Approach 2:
The patent redefines the role of the mediator from a trusted entity that handles sensitive data to an untrusted entity that only processes encrypted data. This transformation maintains the communication simplicity of having a central coordinator while eliminating the security vulnerability of trusting a third party with sensitive information.
4Reliability
If conventional encryption methods are used for securing multi-party data, then data privacy is protected, but computational complexity increases due to multiple encryption layers
Solution Approach 1:
The patent implements nested encryption where data is encrypted with one party's public key, then the resulting ciphertext is encrypted with another party's public key, and so on. This nested structure allows the mediator to collect and process encrypted data from multiple parties without requiring complex coordination, as each layer of encryption independently protects against that specific party's compromise.
Solution Approach 2:
The mediator simplifies the handling of multi-layer encrypted data by collecting all encrypted submissions, performing computations on the encrypted values, and managing the decryption process centrally. This intermediary approach reduces the complexity that would otherwise be distributed across all parties in a peer-to-peer model.
Data Source
AI summary
The present disclosure discloses a method comprising: dividing, by a computing device at a first party among a plurality of parties, local data into a plurality of data segments; recursively encrypting, by the computing device, each data segment using a plurality of public keys corresponding to the plurality of parties and a mediator; sharing, by the computing device, the local data comprising the encrypted plurality of data segments with the mediator; anonymizing, by the computing device, aggregated local data received from the mediator; and communicating, by the computing device from the mediator, a global sum that preserves privacy of the plurality of parties in a multi-party environment, wherein the global sum is computed by the mediator based on the collection of data segments that are decrypted recursively using the private key corresponding to each party and the private key corresponding to the mediator.