Big Data: Privacy and Intellectual Property in a Comparative Perspective
Bibliographic record
Abstract
Big Data is the fastest technology trend of the last few years. Its promises ranges from a philosophical revolution to a massive boost to business and innovation. These great expectations come along with risks and fears about the dissolution of the traditional categories of privacy and anti-competitive effects on business. In particular, the dark side of Big Data concerns the incremental adverse effect on privacy, the notorious predictive analysis and its role as an effective barrier for the market. The first stage of the legal analysis consists in an operative definition of Big Data, useful to build up a common background for further legal speculations. Data deluge, the exponential growth of data produced on a daily basis in every field of knowledge, is considered the base for the existence of a Big Data world. As a result, the practical applications of the data analysis involve healthcare, smart grids, mobile devices, traffic management, retail and payments. Moreover, the role played by open data initiatives around the world may strongly synergize with Big Data. The main issues identified are studied through a comparative analysis of three different legal systems: US, Canada and EU. Notably, the origins of privacy in the US are considered to sketch the line toward the US policy is moving. On the other hand, the current draft of the General Data Protection Regulation on EU level is completely changing the landscape of data protection. Finally, the European influence is clearly perceivable on the Canadian legislation. Although the level of protection granted slightly differ, it is still possible to identify the common consequences of the rise of Big Data on the legal categories. In particular, the fall and redefinition of the concept of PII, the question whether the binomial anonymization/re-identification may still exist, data minimization and individual control. The attempt of this paper is to provide a multi-layered solution given to the so-called Big Data conundrum. Consequently, the single layers are represented by: proactive privacy protection methods, self regulation and transparency, a model of due process applicable to data processing. The second part of this paper is dedicated to answer a challenging question: whether or not IP traditional categories are suited to work with Big Data practices. This section of the work focuses on the different practices used in the market before summing up the common traits. In this way, pros and cons of the application of the traditional IP legal constructs are considered having regard of a general category of Big Data practice. Eventually, the lack in the current legal landscape of an IP construct able to meet the needs of the industry suggests to imagine the main characteristics of a new dataright.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".