Concern 444 · Working draft · editorial review in progress
Homomorphic-encryption performance
Encrypted-computation performance limits are the extra time and resources that can be required to run calculations while the input remains encrypted.
Understanding the concern
Homomorphic encryption allows certain computations on protected data without first revealing the original values to the computing service. This can reduce exposure, but the calculations differ from ordinary processing. The practical cost depends on the task, method, hardware and required precision.
A useful analogy is working with sealed instructions through a specially designed machine: privacy is preserved in one part of the process, but the machine may need more steps. It is too broad to say the technology is always too slow. Some workloads are more suitable than others, and protecting inputs does not automatically make every released result private.
An example
A privacy-preserving prototype works correctly but cannot yet meet the response time needed for its intended service.
Illustrative unless identified as a documented case.
Evidence and its limits
NIST explains the capability and complementary protections. Performance needs workload-specific benchmarks; no universal slowdown is asserted.
Brandao and Peralta; NIST · Technical explainer
Privacy-Enhancing Cryptography to Complement Differential Privacy ↗ (opens in a new tab)Computing on protected inputs and how different privacy technologies address different risks.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- OECD AI Principles (opens in a new tab)
framework
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
Has the actual workload been tested for speed, cost and privacy of both inputs and outputs?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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