How can you maintain data privacy and security when using data collection tools?

Healthcare, by nature, is a conservative space, and for compelling reasons. We are entrusted with incredibly sensitive data. Maintaining public trust is a key responsibility. While AI driven data collection tools are game-changers, not all are made equal. Prioritize tools that offer stringent security features—encryption, authorization, and the likes. Above all, the underlying databases must have segmented data, preventing reconstruction of patient-specific details. Even when sharing data, the preference should be pseudo-anonymization. Leveraging 'trusted third parties' (TTPs) ensures security of the necessary keys to de-anonymize data. Data collection should be purpose-driven, preserving the dignity and trust of those it belongs to.

Energy sector, OT cybersecurity experience: Use the triad- Technology, People, Process. 1. Identify the right tech- while in IT it can be tools, AI,ML, antivirus, firewall; in OT it also includes Product encryption (eg- ProductCERT) and its update. 2. People readiness- By providing right access control, trainings, certification. 3. Process- with above two, the process needs to be upgraded. Includes Authorisation levels, policies etc. Here you use the Energy PLC intelligence to design the right SCADA requirements and put it into the Triad. With this you do your regular assessment and ensure data security.

When selecting data collection tools, prioritize those that align with legal, ethical, and security best practices. Opt for tools with robust encryption, access controls, and activity logging to safeguard data. Analyze costs, scalability, and features to ensure the tool fits your current and future needs. Vet providers thoroughly and avoid any with concerning privacy histories. For custom tools, perform security audits and penetration testing. Implement layered security with multi-factor authentication, VPNs, firewalls, and endpoint protection. With diligence in choosing and setting up tools, you can collect quality data while upholding privacy standards.

From a (training) data privacy perspective, it is important to understand that trained models can also leak properties of the underlying data. There are 2 categories of ML privacy attacks: membership inference and property inference attacks. A membership inference attack refers to a basic privacy violation, where the attacker’s objective is to determine if a specific user data item was present in the training dataset. In property inference attacks, the attacker’s objective is to reconstruct properties of a user's dataset. Black box attacks (when the attacker has only access to the inference API, and not the underlying data) are also possible where the attacker can query the API, and observe the relationships between inputs-outputs.

When collecting data from valuable research study participants there is a tension between wanting to collect as much information as possible to (later) get the maximum benefit of the effort vs risking exposing the study participants' identity and wasting time and resources collecting and storing samples that will never be used. De-identification, anonymization, and pseudo-randomization, as well as having a thoughtful a-priori data collection, QA/QC, storage, maintenance, and retrieval plan in place, is key.

Adopting a minimalist approach to data collection and retention is imperative for upholding privacy. Carefully evaluate what data is essential for your goals and avoid superfluous or sensitive attributes. Seek informed consent when collecting personal data and be transparent about usage. Employ anonymization by excluding or masking identifiers and using aggregation to dilute granular details. Restrict data access to only authorized personnel through strict permission protocols. Set policies to automatically purge unneeded data per legal and compliance standards. Use test data when possible and redact screenshots. Enable data encryption whenever stored or transmitted.

Robust data privacy requires comprehensive policies and vigilant adherence to security best practices. Protect networks with firewalls, intrusion detection, and VPNs. Encrypt stored and transmitted data end-to-end. Use strong, unique passwords and multi-factor authentication. Continuously patch and update systems. Allow only necessary access to approved personnel. Backup data regularly with versioning. Validate inputs and implement checks to ensure quality. Document the data lineage thoroughly. Provide staff training on security protocols and ethics. Stay current on legal and regulatory requirements like GDPR and HIPAA. Seek consent and inform data subjects on usage. Anonymize data when possible.

Ongoing vigilance through monitoring and auditing is imperative to identify and address privacy and security risks in data collection. Tools with robust logging enable constant tracking of access, changes and usage. Configure alerts on suspicious activities. Conduct periodic penetration testing to find vulnerabilities. Review policies and procedures regularly to close gaps. Validate consent records and data anonymization mechanisms. Assess third-party risks in handling data. Document and report all incidents and remediate promptly. Achieve compliance with regulations like GDPR via audits. Training and awareness help create a culture of security. Monitoring and auditing demonstrate diligence in safeguarding data and upholding public trust.

- Risk assessment - Conduct a privacy impact assessment and data protection risk assessment before collecting data. - Access controls - Implement role-based access controls, separation of duties, and least privilege principles to allow only necessary access. - Third party oversight - Vet cloud providers, contractors, and other third parties for security and ensure contracts protect data. - Incident response plan - Have a plan for quickly detecting, investigating, and remediating data breaches or exposures. - Data destruction - Securely delete data when no longer needed per policy, don't just delete references. - International compliance - Understand transborder data flow regulations when data crosses borders.