Cryptographers solve decades-old privacy problem
Homomorphic encryption and related techniques like private information retrieval and zero-knowledge proofs promise computations on encrypted data, enabling services such as search, analytics, or identity verification without exposing underlying information. Commenters explore potential applications—from medical imaging and cloud data processing to private web search and digital cash—while emphasizing that current schemes remain extremely slow, resource-intensive, and mostly impractical at scale. The overall view is that these are intellectually significant breakthroughs that may reduce the need for institutional trust, but real-world deployment will depend on major efficiency gains and clear economic incentives.
Overall reaction
- Many commenters are excited about homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs), calling them among the most promising privacy technologies.
- Others are skeptical, viewing current schemes as academic or impractical, with huge overhead and limited real-world deployment.
Example applications discussed
- Cloud computing on encrypted data: fraud detection, analytics on proprietary or sensitive datasets, healthcare/X-ray analysis, and collaborative inter-company computations without mutual trust.
- Privacy-preserving services: search engines that don’t see queries, Google-like personalization, ad bidding without direct user-data access, encrypted document storage (Drive/Docs) with server-side computation.
- Selective disclosure: proving age or sufficient funds without revealing full ID or balances; “over 21” proofs; tax computation without exposing raw financial data.
- ML/LLM use: running proprietary models on user-encrypted inputs so neither side reveals secrets.
- Niche ideas: grading student notebooks locally, DRM-like control, voting systems with auditable encrypted tallies, hedge-fund-style encrypted datasets, private thumbnails/image processing.
Technical clarifications
- FHE allows computation directly on ciphertext, returning encrypted results; only the key holder can decrypt.
- To avoid side channels, schemes must behave in constant-time style: touch all bits, fixed-size outputs, no data-dependent branching; this inherently increases cost.
- Current constructions often require fixed circuits, limited looping, and can’t leak information via variable output size.
Performance and feasibility
- Several comments stress enormous overhead: petabyte-scale preprocessed databases, operations comparable to or slower than very early computers.
- Benchmarks cited include multi-second operations on small integers and multi-machine clusters to answer a single private search query.
- Some argue these costs might still be acceptable for rare, high-value computations; others say another breakthrough is needed, not just engineering.
Economics, trust, and adoption
- Debate over whether reduced trust is a compelling economic driver versus contracts, regulation, and existing mitigations.
- Some see potential in lowering compliance/risk costs; others argue that unless trust is catastrophically expensive, FHE will remain niche.
Related technologies and deployments
- ZKPs are noted as already impactful in blockchains (privacy coins, ZK-rollups).
- Private Information Retrieval and trusted hardware (e.g., SGX) are discussed as alternative or complementary approaches, each with their own trust assumptions and limitations.