SmartPrivacy for the Smart Grid: embedding privacy into the design of electricity conservation
Bibliographic record
Abstract
The 2003 blackout in the northern and eastern U.S. and Canada which caused a $6 billion loss in economic revenue is one of many indicators that the current electrical grid is outdated. Not only must the grid become more reliable, it must also become more efficient, reduce its impact on the environment, incorporate alternative energy sources, allow for more consumer choices, and ensure cyber security. In effect, it must become “smart.” Significant investments in the billions of dollars are being made to lay the infrastructure of the future Smart Grid. However, the authors argue that we must take great care not to sacrifice consumer privacy amidst an atmosphere of unbridled enthusiasm for electricity reform. Information proliferation, lax controls and insufficient oversight of this information could lead to unprecedented invasions of consumer privacy. Smart meters and smart appliances will constitute a data explosion of intimate details of daily life, and it is not yet clear who will have access to this information beyond a person’s utility provider. The authors of this paper urge the adoption of Dr. Ann Cavoukian’s conceptual model ‘SmartPrivacy’ to prevent potential invasions of privacy while ensuring full functionality of the Smart Grid. SmartPrivacy represents a broad arsenal of protections, encapsulating everything necessary to ensure that all of the personal information held by an organization is appropriately managed. These include: Privacy by Design; law, regulation and independent oversight; accountability and transparency; market forces, education and awareness; audit and control; data security; and fair information practices. Each of these elements is important, but the concept of Privacy by Design represents its sine qua non. When applying SmartPrivacy to the Smart Grid, not only will the grid be able to, for example, become increasingly resistant to attack and natural disasters—it will be able to do so while also becoming increasingly resistant to data leakage and breaches of personal information. The authors conclude that SmartPrivacy must be built into the Smart Grid during its current nascent stage, allowing for both consumer control of electricity consumption and consumer control of their personal information, which must go hand in hand. Doing so will ensure that consumer confidence and trust is gained, and that their participation in the Smart Grid contributes to the vision of creating a more efficient and environmentally friendly electrical grid, as well as one that is protective of privacy. This will result in a positive-sum outcome, where both environmental efficiency and privacy can coexist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".