Update: 2023/2/4
The principle of "data minimization" means that a data controller should limit the collection of personal information to what is directly relevant and necessary to accomplish a specified purpose. They should also retain the data only for as long as is necessary to fulfil that purpose.
Original paper: The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for Urban Big Data Analytics.
What is least sensing?
When conducting urban big data analysis involving personal data, a data processing entity must sense and collect only the minimum information necessary for the specified analysis purpose.How to interpret "minimum"?
The first and perhaps most intuitive explanation of minimum is on the data quantity, i.e., sensing the smallest amount of data required for the purpose. Actually, other interpretations exist, such as data precision, data sensitivity, and data predictability.Search for some FL-related papers.
A SDC repository: AI-SDC
- Data quantity minimization
- Data retention heuristics focusing on performance-related properties of data.
- Retain the most recent data while discarding old data.
- Data quality minimization: aims to reduce the quality of data without reducing its overall quantity.
- Performance-based data minimization
- Global data minimization: aims to minimize per-user data collection subject to meeting a target mean performance across users.
- Per-user data minimization: aims to minimize per-user data collection subject to meeting a target performance for the minimum across all users.
- Breadth-based data minimization: aims to minimize the number of features.
- Depth-based data minimization: aims to minimize the overall amount of data collected for one data modality.
- Runtime data minimization: aims to minimize newly collected data for analysis or prediction.
- Personalized data minimization: aims to allow different users to reveal more or less about different aspects of their lives based on their own personal preference.
- Query-driven data minimization:
- Language-based data minimization:
| Title | Stage | Link |
|---|---|---|
| Limiting data collection in application forms: A real-case application of a founding privacy principle. | Data collection | PST2012 |
| Privacy Architectures: Reasoning About Data Minimisation and Integrity. | Data collection | STM2014 |
| Data Minimisation: A Language-Based Approach. | Data collection | SEC2017 |
| Towards Query-Driven Data Minimization. | Data collection | LWDA2018 |
| Student Success Prediction and the Trade-Off between Big Data and Data Minimization. | Data collection | DeLFI2018 |
| Query-Driven Data Minimization with the DataEconomist. | Data collection | EDBT2019 |
| A Data Minimization Model for Embedding Privacy into Software Systems. | Data collection | ComSec2019 |
| Operationalizing the Legal Principle of Data Minimization for Personalization. | Data collection | SIGIR2020 |
| Fair Inputs and Fair Outputs The Incompatibility of Fairness in Privacy and Accuracy. | Data collection | UMAP2020 |
| Embedding Personal Data Minimization Technologies in Organizations: Needs, Vision and Artifacts. | Data collection | ICEGOV2021 |
| A Lightweight Scheme Exploiting Social Networks for Data Minimization According to the GDPR. | Data collection | TCSS2021 |
| How to address data privacy concerns when using social media data in conservation science. | Data collection | ConBio2021 |
| Towards a Formal Approach for Data Minimization in Programs. | Data collection | DPM/CBT2021 |
| Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs. | Data collection | ICWE2022 |
| Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization. | Data collection | FAccT2022 |
| Practical Data Access Minimization in Trigger-Action Platforms. | Data collection | USENIXSS2022 |
| I Prefer not to Say: Operationalizing Fair and User-guided Data Minimization. | Data collection | CoRR2022 |
| Privacy for Free: How does Dataset Condensation Help Privacy. | Data collection | ICML2022 |
| Censoring Representations with an Adversary. | Data inference | ICLR2016 |
| Supporting the Design of Privacy-Aware Business Processes via Privacy Process Patterns. | Data inference | RCIS2017 |
| Detecting Conflicts Between Data-Minimization and Security Requirements in Business Process Models. | Data inference | ECMFA2018 |
| Mobile Sensor Data Anonymization. | Data inference | IoTDI2019 |
| Generating Optimal Privacy-Protection Mechanisms via Machine Learning. | Data inference | CORR2019 |
| A semi-automated BPMN-based framework for detecting conflicts between security, data-minimization, and fairness requirements. | Data inference | SSM2020 |
| A Siamese Adversarial Anonymizer for Data Minimization in Biometric Applications. | Data inference | EuroS&P2020 |
| A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics. | Data inference | ITJ2020 |
| Data Minimization for GDPR Compliance in Machine Learning Models. | Data inference | AIEthics2022 |
| Auditing Black-Box Prediction Models for Data Minimization Compliance. | Data audit | NIPS2021 |
| Auditing Algorithms: On Lessons Learned and the Risks of Data Minimization. | Data audit | AIES2021 |
| AI auditing and impact assessment: according to the UK information commissioner’s office. | Data audit | AIEthics2021 |
| Privacy 3.0 := Data Minimization + User Control + Contextual Integrity. | Other | InfTech2011 |
| From Data Minimization to Data Minimummization. | Other | DPIS2013 |
| Mo'Data, Mo'Problems? Personal Data Mining and the Challenge to the Data Minimization Principle. | Other | FPF2013 |
| Regime Change: Enabling Big Data through Europe's New Data Protection Regulation. | Other | SciTech2016 |
| A Process for Data Protection Impact Assessment Under the European General Data Protection Regulation. | Other | APF2016 |
| The European General Data Protection Regulation: challenges and considerations for iPSC researchers and biobanks. | Other | FutMed2017 |
| The Legal Challenges of Big Data: Putting Secondary Rules First in the Field of EU Data Protection. | Other | EDPL2017 |
| Towards a Roadmap for Privacy Technologies and the General Data Protection Regulation: A Transatlantic Initiative. | Other | APF2018 |
| Reviving Purpose Limitation and Data Minimisation in DataDriven Systems. | Other | TechReg2021 |
| A survey of data minimisation techniques in blockchain-based healthcare. | Survey | ComNet2022 |
| Data Minimisation Potential for Timestamps in Git: An Empirical Analysis of User Configurations. | ? | Sec2022 |
| A Refactoring for Data Minimisation Using Formal Verification. | ? | ISoLA2022 |
| Data Minimisation as Privacy and Trust Instrument in Business Processes. | ? | BPMW2020 |
| Data Minimisation in Communication Protocols: A Formal Analysis Framework and Application to Identity Management. | ? | IJIS2014 |
| TRIPLEX: verifying data minimisation in communication systems. | ? | CCS2013 |
| Monitoring Data Minimisation. | ? | CoRR2018 |
| Personality Is Revealed During Weekends: Towards Data Minimisation for Smartphone Based Personality Classification. | ? | INTERACT2019 |
| Prevention of Personally Identifiable Information Leakage in E-commerce via Offline Data Minimisation and Pseudonymisation. | ? | IJISRT2021 |
| Engineering privacy by design reloaded. | ? | APC2015 |