Measuring personal information exposure in the mobile and IoT environments
Bibliographic record
Abstract
Mobile and Internet of Things (IoT) devices are increasingly present in our everyday lives. They are equipped with a wide array of rich sensors and offer ubiquitous connectivity. These properties make the devices perfect candidates for data harvesting and privacy invasion over the Internet. However, these devices are usually locked down by OSes and carriers, making it difficult for the research community---and average users---to understand and mitigate online privacy concerns like disclosure of information to third parties. I argue that improving online privacy requires building systems that identify and control private information transmission. Our key observation is that a privacy dissemination must (by definition) occur over the network, so it is possible to detect and mitigate the leakage in the network traffic. Using this approach, the first challenge is how to detect privacy dissemination in network traffic. To address the problem, I developed a system called ReCon to apply machine learning algorithms to reliably identify Personally Identifiable Information (PII) without knowing the PII values in advance. Second, to quantify and understand the online privacy, I conducted experiments in multiple platforms (mobile applications in Android, iOS, Windows and mobile browsers) and studied how privacy exposure from mobile apps evolved over the range of eight years. Third, I developed a holistic approach to detect media exposure from Android mobile apps. Lastly, I extend such analysis to IoT devices and developed techniques to quantify information exposure---even for encrypted traffic---with semi-automated experiments. The goal of my work is to enable any Internet user to understand and control the private information exposed by mobile and IoT devices over the Internet. My work has enabled substantial progress in the increasingly important area of online privacy, has been cited and used by regulators and investigative journalists, and provides opportunities to improve privacy for individual users and organizations.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".