Data Privacy Evaluation

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB

Isabel Barberá: "This document provides practical guidance and tools for developers and users of Large Language Model (LLM) based systems to manage privacy risks associated with these technologies. The risk management methodology outlined in this document is designed to help developers and users systematically identify, assess, and mitigate privacy and data protection risks, supporting the responsible development and deployment of LLM systems. This guidance also supports the requirements of the GDPR Article 25 Data protection by design and by default and Article 32 Security of processing by offering technical and organizational measures to help ensure an appropriate level of security and data protection. However, the guidance is not intended to replace a Data Protection Impact Assessment (DPIA) as required under Article 35 of the GDPR. Instead, it complements the DPIA process by addressing privacy risks specific to LLM systems, thereby enhancing the robustness of such assessments. Guidance for Readers > For Developers: Use this guidance to integrate privacy risk management into the development lifecycle and deployment of your LLM based systems, from understanding data flows to how to implement risk identification and mitigation measures. > For Users: Refer to this document to evaluate the privacy risks associated with LLM systems you plan to deploy and use, helping you adopt responsible practices and protect individuals’ privacy. " >For Decision-makers: The structured methodology and use case examples will help you assess the compliance of LLM systems and make informed risk-based decision" European Data Protection Board

Let's make it clear: We need more frameworks for evaluating data protection risks in AI systems. As I delve into this topic, more and more new papers and risk assessment approaches appear. One of them is described in the paper titled "Rethinking Data Protection in the (Generative) Artificial Intelligence Era." 👉 My key takeaways: 1️⃣ Begin by identifying the data that should be protected in AI systems. Authors recommend focusing on the following: •  Training Datasets •  Trained Models •  Deployment-integrated Data (e.g., protect your internal system prompts and external knowledge bases like RAG). ❗ I loved this differentiation and risk assessment, as if, for example, an adversary discovers your system prompts, they might try to exploit them. Also, protecting sensitive RAG data is essential. •  User prompts (e.g., besides prompts protection, add transparency and let users know if prompts will be logged or used for training). •  AI-generated Content (e.g., ensure traceability to understand its provenance if used for training, etc.). 2️⃣ Authors also introduce an interesting taxonomy of data protection areas to focus on when dealing with generative AI: •  Level 1: Data Non-usability. Ensures that specified data cannot contribute to model learning or predicting in any way by using strategies that block any unauthorized party from using or even accessing protected data (e.g., encryption, access controls, unlearnable examples, non-transferable learning, etc.) •  Level 2: Data Privacy-preservation. Here, the focus is on how the training can be performed with enhanced privacy techniques (PETs): K-anonymity and L-diversity schemes, differential privacy, homomorphic encryption, federated learning, and split learning. •  Level 3: Data Traceability. This is about the ability to track the origin, history, and influence of data as it is used in AI applications during training and inference. This capability allows stakeholders to audit and verify data usage. This can be categorised into intrusive (e.g., digital watermarking with signatures to datasets, model parameters, or prompts) and non-intrusive methods (e.g., membership inference, model fingerprinting, cryptographic hashing, etc.). •  Level 4: Data Deletability. This is about the capacity to completely remove a specific piece of data and its influence from a trained model (authors recommend exploring unlearning techniques that specifically focus on erasing the influence of the data in the model, rather than the content or model itself). ------------------------------------------------------------------------ 👋 I'm Vadym, an expert in integrating privacy requirements into AI-driven data processing operations. 🔔 Follow me to stay ahead of the latest trends and to receive actionable guidance on the intersection of AI and privacy. ✍ Expect content that is solely authored by me, reflecting my reading and experiences. #AI #privacy #GDPR

The Office of the Australian Information Commissioner has published the "Privacy Foundations Self-Assessment Tool" to help businesses evaluate and strengthen their privacy practices. This tool is designed for organizations that may not have in-house privacy expertise but want to establish or improve how they handle personal information. The tool is structured as a questionnaire and an action planning section that can be used to create a Privacy Management Plan. It covers key #privacy principles and offers actionable recommendations across core areas of privacy management, including: - Accountability and assigning responsibility for privacy oversight. - Transparency through clear external-facing privacy notices and policies. - Privacy and #cybersecurity training for staff. - Processes for identifying and managing privacy risks in new projects. - Assessing third-party service providers handling personal data. - Data minimization practices and consent management for sensitive information. - Tracking and managing use and disclosure of personal data. - Ensuring opt-out options are provided and honored in direct marketing. - Maintaining an up-to-date inventory of personal data holdings. - Cybersecurity and data breach response. - Secure disposal or de-identification of data when no longer needed. - Responding to privacy complaints and individual rights requests. This self-assessment provides a maturity score based on the responses to the questionnaire and tailored recommendations to support next steps.

This is how I Conduct Privacy Audits as a Consultant. Privacy audits are essential for organizations aiming to stay compliant with regulations and protect personal data. Why Privacy Audits Matter: A thorough audit doesn’t just tick compliance boxes—it strengthens trust, reduces risks, and ensures data is handled responsibly. Steps to consider --- Step 1: Preparation is Key 🔹 Understand the Scope: I start by discussing the client's objectives—Are we assessing GDPR compliance? Kenya’s Data Protection Act? 🔹 Gather Documentation: Policies, contracts, and past audit reports help me lay the foundation. 🔹 Plan the Audit: A clear roadmap ensures efficiency, covering timelines, stakeholders, and methods. Step 2: Mapping Data Flows 🔹 Follow the Data: I map how personal data is collected, processed, shared, and stored. 🔹 Classify the Data: Is it sensitive, personal, or anonymized? Knowing this guides my compliance checks. Step 3: Reviewing Policies 🔹 Policies Under the Microscope: Are the privacy notices comprehensive? Are Data Processing Agreements in place? 🔹 Handling DSARs: I assess how well the organization manages data subject requests and consent. Step 4: Technical Check-Up 🔹 Data Security Measures: Are encryption, access controls, and secure storage practices implemented? 🔹 Vulnerability Assessment: I look for risks like weak passwords or unsecured APIs. Step 5: Stakeholder Interviews 🔹 Understand the Practice: Policies are one thing, but what’s happening on the ground? Talking to employees and IT teams bridges the gap. 🔹 Evaluate Awareness: Is there a culture of data protection? Step 6: Gap Analysis & Recommendations 🔹 Highlight Gaps: I identify areas of non-compliance and risks. 🔹 Provide Solutions: Practical, prioritized actions are key—policies to update, processes to improve, or risks to mitigate. Step 7: Reporting and Follow-Up 🔹 Deliver Insights: A concise report with findings and clear recommendations ensures actionability. 🔹 Continuous Improvement: Privacy is a journey. I often assist in implementing recommendations and schedule follow-ups. ------- To partner with me please email sakinyi717@gmail.com #privacyaudits #dataprotection

In an era where data sharing is essential and concerning, six fundamental techniques are emerging to protect privacy while enabling valuable insights. Fully Homomorphic Encryption involves encrypting data before being shared, allowing analysis without decoding the original information, thus safeguarding sensitive details. Differential Privacy adds noise variables to a dataset, making decoding the initial inputs impossible, maintaining privacy while allowing generalized analysis. Functional Encryption provides selected users a key to view specific parts of the encrypted text, offering relevant insights while withholding other details. Federated Analysis allows parties to share only the insights from their analysis, not the data itself, promoting collaboration without direct exposure. Zero-Knowledge Proofs enable users to prove their knowledge of a value without revealing it, supporting secure verification without unnecessary exposure. Secure Multi-Party Computation distributes data analysis across multiple parties, so no single entity can see the complete set of inputs, ensuring a collaborative yet compartmentalized approach. Together, these techniques pave the way for a more responsible and secure data management and analytics future. #privacy #dataprotection

Evaluation of Privacy-aware Support Vector Machine (SVM) Learning using Homomorphic Encryption The requirement for privacy-aware machine learning increases as we continue to use PII (Personally Identifiable Information) within machine training. To overcome these privacy issues, we can apply Fully Homomorphic Encryption (FHE) to encrypt data before it is fed into a machine learning model. This involves creating a homomorphic encryption key pair, and where the associated public key will be used to encrypt the input data, and the private key will decrypt the output. But, there is often a performance hit when we use homomorphic encryption, and so this paper evaluates the performance overhead of using the SVM machine learning technique with the OpenFHE homomorphic encryption library. This uses Python and the scikit-learn library for its implementation. The experiments include a range of variables such as multiplication depth, scale size, first modulus size, security level, batch size, and ring dimension, along with two different SVM models, SVM-Poly and SVM-Linear. Overall, the results show that the two main parameters which affect performance are the ring dimension and the modulus size, and that SVM-Poly and SVM-Linear show similar performance levels. You can read the paper here: https://lnkd.in/d_DX6nqR

🚨 Big News for AI Governance & Privacy! 🚨 National Institute of Standards and Technology (NIST) has just released Special Publication 800-226: Guidelines for Evaluating Differential Privacy Guarantees, a crucial document for anyone dealing with privacy-preserving AI systems and data governance. 📜🔍 Why does this matter? Because AI governance needs clear, standardized privacy protections, and differential privacy is at the core of modern data protection strategies. 💡 Key Takeaways from the Executive Summary: ✅ Differential privacy provides a mathematical guarantee that individual data contributions remain protected—even in massive datasets. ✅ Balancing privacy & utility is crucial: More noise = better privacy but reduced accuracy. The guidelines outline best practices for managing this trade-off. ✅ Real-world application matters: This isn’t just theory. The guide includes practical steps for deploying differential privacy in machine learning, synthetic data generation, and analytics. ✅ Standardization & Certification: NIST is laying the groundwork for certifiable differential privacy guarantees, which will be a game-changer for organizations handling sensitive data. ✅ Security matters: Even the best privacy techniques are meaningless if data security fails. The guide stresses access control, secure implementation, and continuous auditing to avoid vulnerabilities. 🔎 Why AI Governance Experts Should Care AI systems rely on data, but trust in these systems depends on how privacy is safeguarded. Without structured privacy guarantees, AI governance becomes a guessing game. This NIST publication provides a rigorous, standardized approach that AI policymakers, compliance officers, and technical teams can align with. #AIPrivacy #AIGovernance #DifferentialPrivacy #NIST #DataSecurity __________________________________ Did you like this post? Connect or Follow 🎯 Jakub Szarmach, AIGP, CIPM Szarmach Want to see all my posts? Ring that 🔔

Privacy Impact Assessments, or as we affectionately refer to them as PIAs, proactively identify and mitigate privacy risks in new or modified data processing activities. Their primary objective is to identify privacy risks and ensure compliance with data protection laws. By identifying risks and creating a mitigation build, companies build consumer trust by demonstrating a steadfast commitment to protecting personal data. Sometimes, the dots of when to conduct one, what to look for, and how to mitigate the risks can be hard and there can be crucial misses during this methodical process. When was the last time you reviewed your PIA process? At Red Clover Advisors we’ve witnessed common PIA missteps companies make. It’s a complex process, and that’s why we love working with businesses on when to complete PIAs. One of our favorite steps in the PIA process is creating privacy threshold assessments (PTA). A PTA is a checklist of questions to quickly determine when a full PIA is needed. As you navigate through your PIA process, it's essential to stay vigilant and avoid these common PIA pitfalls: 🔁 No processes and policies to support PIAs. 🤔 Not knowing when to conduct a PIA. Use a Privacy Threshold Assessment to determine risk and if a whole PIA is required. 📃 Sole dependence on automated software or templates without adjusting the PIA template or manually reviewing the results. 🤦♀️ Not involving Privacy Subject Matter Experts (SMEs) to review it and identify any risks. ⚡ Inadequate Risk Mitigation and failing to develop an accountable plan and/or having someone manage the plan. 👩🏽🏫 Failure to educate key employees on what the PIA process is and why it is important to regularly review the PIA. #privacy #dataprivacy #privacyimpactassessments #PIA