IoT Big Data Security and Privacy vs. Innovation
Bibliographic record
Abstract
In this paper, we address the conflict in the collection, use and management of Big Data at the intersection of security and privacy requirements and the demand of innovative uses of the data. This problem is exaggerated in the context of the Internet of Things (IoT). We propose a three-part decomposition of the design space, in order to clarify requirements and constraints. To reach this final analysis, we begin by clarifying the challenges in the design space: (1) there is little agreement on what is meant by IoT, and in particular the security and privacy implications of different definitions; (2) we then consider the requirement and constraints on the big data that result from various IoT system designs; (3) in parallel, we examine the intricacies of the demand for innovation from the both the legal and economic perspectives. In this context, we then can decompose the set of drivers and objectives for security/privacy of data as well as innovation into (1) the regulatory and social policy context, (2) economic and business context, and (3) technology and design context. By identifying these distinct objectives for the design of IoT Big Data management, we propose that more effective design and control is possible at the intersection of these forces, through an iterative process of review and redesign.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".