Futures

Enhancing Trust in AI Through Modern Cryptographic Techniques, (from page 20230708.)

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Summary

The integration of AI into daily life raises concerns about trust, particularly regarding the authenticity of content, data privacy, and the integrity of AI outputs. While trust in AI usage for good is a regulatory issue, technology can address other trust concerns through cryptography. Cryptographic signatures can help verify the authenticity of content, ensuring that authors cannot impersonate others, thus combating deepfakes. Fully Homomorphic Encryption (FHE) allows users to maintain data privacy while using cloud AI services by processing encrypted data without decryption. Zero-Knowledge Proofs (ZKPs) enable users to verify AI-generated results without manipulation by providing cryptographic proof of the processing method used. Overall, leveraging these cryptographic technologies can create a safer AI environment that is authenticated, verifiable, and private.

Signals

name description change 10-year driving-force relevancy
Rise of Cryptographic Trust Solutions Adoption of cryptographic methods to enhance trust in AI-generated content. Shift from blind trust in AI outputs to verified, cryptographically signed results. In 10 years, cryptographic signatures may become standard for all digital content, ensuring authenticity. The need for trust in AI technologies, driven by increasing concerns over misinformation and manipulation. 4
Digital Identity Transformation Emergence of digital identities linked to cryptographic keys and wallets. Transition from anonymous online interactions to verified digital identities using cryptography. Digital identities may dominate social media and online services, changing how users interact and verify authenticity. The push for accountability and trust in online interactions, highlighted by regulatory developments. 5
Homomorphic Encryption in AI Services Implementation of homomorphic encryption for privacy in AI processing. Move from data visibility to complete data privacy during AI queries and processing. AI services may operate entirely on encrypted data, safeguarding user privacy while maintaining functionality. Growing demand for privacy and data protection in the age of digital services and AI. 5
Zero-Knowledge Proofs for AI Verification Utilization of zero-knowledge proofs to validate AI outputs without revealing data. Change from opaque AI processes to transparent, verifiable outputs through cryptographic proofs. AI interactions could become fully transparent, allowing users to verify outputs independently and securely. Increasing scrutiny of AI systems and the need for transparency to build user trust. 4

Concerns

name description relevancy
Trust in AI Usage Concerns regarding the potential misuse of AI technology for harmful purposes, requiring regulatory oversight. 4
Authenticity of Content The risk of misleading or false AI-generated content affecting public perception and trust, necessitating cryptographic signatures. 5
Data Privacy The challenge of maintaining user data privacy while using AI services, highlighting the importance of homomorphic encryption. 5
Manipulation of AI Results Concerns that AI companies may manipulate outputs, indicating a need for zero-knowledge proofs to ensure result integrity. 5
Digital Identity Verification Emerging need for secure digital identities to build trust in online interactions, especially in social media. 4

Behaviors

name description relevancy
Trust through Cryptographic Signatures Utilizing cryptographic signatures for authenticating content to combat misinformation and deepfakes. 5
Digital Identities and Verified Accounts The rise of digital identities and verified accounts on social media to enhance trust in online interactions. 4
Homomorphic Encryption for Data Privacy Employing Fully Homomorphic Encryption to maintain data privacy while using AI services in the cloud. 5
Zero-Knowledge Proofs in AI Implementing Zero-Knowledge Proofs to ensure the integrity and authenticity of AI-generated results. 5

Technologies

name description relevancy
Homomorphic Encryption A method that allows computations on encrypted data without decrypting it, ensuring data privacy during processing. 5
Zero-Knowledge Proofs (ZKPs) A cryptographic method that allows one party to prove to another that a statement is true without revealing any information beyond the validity of the statement. 5
Digital Identities and Crypto Wallets The use of verified digital identities and crypto wallets to enhance trust and security in online interactions. 4
ZK for Machine Learning (ZKML) Application of zero-knowledge proofs in machine learning to ensure the integrity and authenticity of AI results. 4

Issues

name description relevancy
Trust in AI-generated content The need for cryptographic signatures to verify the authenticity of AI-generated content and mitigate risks like deepfakes. 5
Digital identities and crypto wallets The rising importance of digital identities, potentially linked to crypto wallets, for verifying online accounts and enhancing trust. 4
Privacy in AI data usage The potential of homomorphic encryption to ensure user data privacy while interacting with AI services in the cloud. 5
Manipulation of AI results The use of zero-knowledge proofs (ZK) to ensure that AI-generated results are authentic and not manipulated by the service provider. 5
Regulatory frameworks for AI ethics The need for government regulations to define ethical AI usage and align AI models with local moral values. 4