Data Privacy Standards in Machine Learning

The EDPB recently published a report on AI Privacy Risks and Mitigations in LLMs.   This is one of the most practical and detailed resources I've seen from the EDPB, with extensive guidance for developers and deployers. The report walks through privacy risks associated with LLMs across the AI lifecycle, from data collection and training to deployment and retirement, and offers practical tips for identifying, measuring, and mitigating risks.   Here's a quick summary of some of the key mitigations mentioned in the report:   For providers: • Fine-tune LLMs on curated, high-quality datasets and limit the scope of model outputs to relevant and up-to-date information. • Use robust anonymisation techniques and automated tools to detect and remove personal data from training data. • Apply input filters and user warnings during deployment to discourage users from entering personal data, as well as automated detection methods to flag or anonymise sensitive input data before it is processed. • Clearly inform users about how their data will be processed through privacy policies, instructions, warning or disclaimers in the user interface. • Encrypt user inputs and outputs during transmission and storage to protect data from unauthorized access. • Protect against prompt injection and jailbreaking by validating inputs, monitoring LLMs for abnormal input behaviour, and limiting the amount of text a user can input. • Apply content filtering and human review processes to flag sensitive or inappropriate outputs. • Limit data logging and provide configurable options to deployers regarding log retention. • Offer easy-to-use opt-in/opt-out options for users whose feedback data might be used for retraining.   For deployers: • Enforce strong authentication to restrict access to the input interface and protect session data. • Mitigate adversarial attacks by adding a layer for input sanitization and filtering, monitoring and logging user queries to detect unusual patterns. • Work with providers to ensure they do not retain or misuse sensitive input data. • Guide users to avoid sharing unnecessary personal data through clear instructions, training and warnings. • Educate employees and end users on proper usage, including the appropriate use of outputs and phishing techniques that could trick individuals into revealing sensitive information. • Ensure employees and end users avoid overreliance on LLMs for critical or high-stakes decisions without verification, and ensure outputs are reviewed by humans before implementation or dissemination. • Securely store outputs and restrict access to authorised personnel and systems.   This is a rare example where the EDPB strikes a good balance between practical safeguards and legal expectations. Link to the report included in the comments.   #AIprivacy #LLMs #dataprotection #AIgovernance #EDPB #privacybydesign #GDPR

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

✴ AI Governance Blueprint via ISO Standards – The 4-Legged Stool✴ ➡ ISO42001: The Foundation for Responsible AI #ISO42001 is dedicated to AI governance, guiding organizations in managing AI-specific risks like bias, transparency, and accountability. Focus areas include: ✅Risk Management: Defines processes for identifying and mitigating AI risks, ensuring systems are fair, robust, and ethically aligned. ✅Ethics and Transparency: Promotes policies that encourage transparency in AI operations, data usage, and decision-making. ✅Continuous Monitoring: Emphasizes ongoing improvement, adapting AI practices to address new risks and regulatory updates. ➡#ISO27001: Securing the Data Backbone AI relies heavily on data, making ISO27001’s information security framework essential. It protects data integrity through: ✅Data Confidentiality and Integrity: Ensures data protection, crucial for trustworthy AI operations. ✅Security Risk Management: Provides a systematic approach to managing security risks and preparing for potential breaches. ✅Business Continuity: Offers guidelines for incident response, ensuring AI systems remain reliable. ➡ISO27701: Privacy Assurance in AI #ISO27701 builds on ISO27001, adding a layer of privacy controls to protect personally identifiable information (PII) that AI systems may process. Key areas include: ✅Privacy Governance: Ensures AI systems handle PII responsibly, in compliance with privacy laws like GDPR. ✅Data Minimization and Protection: Establishes guidelines for minimizing PII exposure and enhancing privacy through data protection measures. ✅Transparency in Data Processing: Promotes clear communication about data collection, use, and consent, building trust in AI-driven services. ➡ISO37301: Building a Culture of Compliance #ISO37301 cultivates a compliance-focused culture, supporting AI’s ethical and legal responsibilities. Contributions include: ✅Compliance Obligations: Helps organizations meet current and future regulatory standards for AI. ✅Transparency and Accountability: Reinforces transparent reporting and adherence to ethical standards, building stakeholder trust. ✅Compliance Risk Assessment: Identifies legal or reputational risks AI systems might pose, enabling proactive mitigation. ➡Why This Quartet? Combining these standards establishes a comprehensive compliance framework: 🥇1. Unified Risk and Privacy Management: Integrates AI-specific risk (ISO42001), data security (ISO27001), and privacy (ISO27701) with compliance (ISO37301), creating a holistic approach to risk mitigation. 🥈 2. Cross-Functional Alignment: Encourages collaboration across AI, IT, and compliance teams, fostering a unified response to AI risks and privacy concerns. 🥉 3. Continuous Improvement: ISO42001’s ongoing improvement cycle, supported by ISO27001’s security measures, ISO27701’s privacy protocols, and ISO37301’s compliance adaptability, ensures the framework remains resilient and adaptable to emerging challenges.

On Protecting the Data Privacy of Large Language Models (LLMs): A Survey From the research paper: In this paper, we extensively investigate data privacy concerns within Large LLMs, specifically examining potential privacy threats from two folds: Privacy leakage and privacy attacks, and the pivotal technologies for privacy protection during various stages of LLM privacy inference, including federated learning, differential privacy, knowledge unlearning, and hardware-assisted privacy protection. Some key aspects from the paper: 1)Challenges: Given the intricate complexity involved in training LLMs, privacy protection research tends to dissect various phases of LLM development and deployment, including pre-training, prompt tuning, and inference 2) Future Directions: Protecting the privacy of LLMs throughout their creation process is paramount and requires a multifaceted approach. (i) Firstly, during data collection, minimizing the collection of sensitive information and obtaining informed consent from users are critical steps. Data should be anonymized or pseudonymized to mitigate re-identification risks. (ii) Secondly, in data preprocessing and model training, techniques such as federated learning, secure multiparty computation, and differential privacy can be employed to train LLMs on decentralized data sources while preserving individual privacy. (iii) Additionally, conducting privacy impact assessments and adversarial testing during model evaluation ensures potential privacy risks are identified and addressed before deployment. (iv)In the deployment phase, privacy-preserving APIs and access controls can limit access to LLMs, while transparency and accountability measures foster trust with users by providing insight into data handling practices. (v)Ongoing monitoring and maintenance, including continuous monitoring for privacy breaches and regular privacy audits, are essential to ensure compliance with privacy regulations and the effectiveness of privacy safeguards. By implementing these measures comprehensively throughout the LLM creation process, developers can mitigate privacy risks and build trust with users, thereby leveraging the capabilities of LLMs while safeguarding individual privacy. #privacy #llm #llmprivacy #mitigationstrategies #riskmanagement #artificialintelligence #ai #languagelearningmodels #security #risks

Exploring the Intersection of AI and GDPR In this article, we navigate through the critical aspects of data protection during the deployment of AI and Large Language Models (LLMs), highlighting real-world challenges and their solutions. Diversity of Personal Data in AI: AI appears in various forms, with chatbots being a common example. Training these chatbots demands comprehensive datasets, typically composed of audio, video, image, or text data. Special categories of data, such as sexual orientation or political beliefs, need extra attention as per Article 9(1) of the GDPR. Legal Bases for Data Processing: Key legal grounds for processing data include consent (Article 6(1)(a) GDPR), legitimate interest (Article 6(1)(f) GDPR), and contractual relationships (Article 6(1)(b) GDPR). National data protection laws can also be relevant, particularly in areas such as public administration, justice, employee data, and research. Obtaining Training Data: The process of acquiring training data necessitates thoughtful handling of consent, especially when dealing with sensitive information. Repurposing existing data for AI training must respect the purpose limitation principle of Article 5(1)(b) GDPR. Navigating Challenges with Scraped Data: Processing data obtained through scraping requires careful scrutiny due to potential source legality issues and the risk of re-identification. Legal bases like legitimate interest (Article 6(1)(f) GDPR) could apply, but careful evaluation is required. Anonymized or Aggregated Data as a Solution: Anonymized data, where identification is practically impossible, is outside the scope of GDPR. However, both anonymization and the creation of aggregated datasets are considered personal data processing, requiring a legal basis, often relying on legitimate interest. Upholding Rights and Data Security: Individuals have rights such as access, correction, deletion, and restriction under GDPR. Challenges can arise with individualizing Big Data and the inherent "Black Box" issue in AI decision-making. Emphasizing Security Measures: Given the risks associated with processing personal data via AI, prioritizing data security is crucial. Conclusion: Implementing generative AI or LLMs under GDPR brings significant data protection considerations. Compliance measures include updating processing records, revising privacy notices, obtaining consent, conducting Data Protection Impact Assessments, implementing Technical Organizational Measures, and potentially signing Data Processing Agreements. In addition, for AI, an Algorithmic Impact Assessment can be performed (see the link below). #AI #GDPR #DataProtection"

Generative AI offers transformative potential, but how do we harness it without compromising crucial data privacy? It's not an afterthought — it's central to the strategy. Evaluating the right approach depends heavily on specific privacy goals and data sensitivity. One starting point, with strong vendor contracts, is using the LLM context window directly. For larger datasets, Retrieval-Augmented Generation (RAG) scales well. RAG retrieves relevant information at query time to augment the prompt, which helps keep private data out of the LLM's core training dataset. However, optimizing RAG across diverse content types and meeting user expectations for structured, precise answers can be challenging. At the other extreme lies Self-Hosting LLMs. This offers maximum control but introduces significant deployment and maintenance overhead, especially when aiming for the capabilities of large foundation models. For ultra-sensitive use cases, this might be the only viable path. Distilling larger models for specific tasks can mitigate some deployment complexity, but the core challenges of self-hosting remain. Look at Apple Intelligence as a prime example. Their strategy prioritizes user privacy through On-Device Processing, minimizing external data access. While not explicitly labeled RAG, the architecture — with its semantic index, orchestration, and LLM interaction — strongly resembles a sophisticated RAG system, proving privacy and capability can coexist. At Egnyte, we believe robust AI solutions must uphold data security. For us, data privacy and fine-grained, authorized access aren't just compliance hurdles; they are innovation drivers. Looking ahead to advanced Agent-to-Agent AI interactions, this becomes even more critical. Autonomous agents require a bedrock of trust, built on rigorous access controls and privacy-centric design, to interact securely and effectively. This foundation is essential for unlocking AI's future potential responsibly.

This Stanford University white paper outlines a comprehensive examination of privacy and data protection challenges in the age of artificial intelligence. It emphasizes the insufficiency of existing privacy laws to address the unique risks posed by AI systems and proposes novel approaches for enhancing data privacy. Three key suggestions are offered: denormalizing data collection by default, focusing on the AI data supply chain, and innovating the management of personal data to improve privacy protections. 1️⃣ Denormalize data collection by default by shifting away from opt-out to opt-in data collection. Data collectors must facilitate true data minimization through “privacy by default” strategies and adopt technical standards and infrastructure for meaningful consent mechanisms. 2️⃣ Focus on the AI data supply chain to improve privacy and data protection. Ensuring dataset transparency and accountability across the entire life cycle must be a focus of any regulatory system that addresses data privacy. 3️⃣ Flip the script on the creation and management of personal data. Policymakers should support the development of new governance mechanisms and technical infrastructure (e.g., data intermediaries and data permissioning infrastructure) to support and automate the exercise of individual data rights and preferences. The paper argues for a shift in regulatory approaches to better protect individual and societal privacy in the AI-dominated landscape, underscoring the urgent need for updated privacy frameworks in the era of exponential data growth and AI advancements. ✍🏻 Dr. Jennifer King, Caroline Meinhardt. Stanford Institute for Human-Centered Artificial Intelligence (HAI). February 2024. ✅ Subscribe to my newsletter and stay at the forefront of groundbreaking studies. Get started here: https://lnkd.in/eR7qichj.

The European Data Protection Board published the report “AI Privacy Risks & Mitigations Large Language Models” to help developers and users systematically identify, assess, and mitigate privacy and data protection risks, supporting the responsible development and deployment of LLM systems. The report starts by explaining how LLMs work, their common applications, and performance evaluation measures. Then, it discusses how #privacy risks emerge across different #LLM service models, emphasizing the importance of understanding data flows throughout the #artificialintelligence lifecycle. Once the risks are identified and classified, the document shows how to prioritize mitigation efforts effectively. It also discusses residual risk acceptance and the iterative nature of risk. This guidance also supports the requirements of Art. 25 of the #GDPR Article (data protection by design and by default) and Art. 32 (security of processing) by offering technical and organizational measures to help ensure an appropriate level of #security and data protection.

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

Emma loved using her health app - it tracked her sleep, steps, and even detected irregular heartbeats. But she hesitated every time she hit "agree" on the privacy terms. Sharing her sensitive health data felt risky. Until Federated Learning changed the game. Instead of sending her personal data to a central server, the AI model came to her phone, trained locally, and only shared 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 - not her actual data. Emma’s heart health predictions improved without sacrificing her privacy. Federated Learning is rewriting the AI playbook: smarter models, safer data. The future of AI is collaborative without being invasive. Federated Learning is a privacy-preserving machine learning technique. Instead of sending users' raw data to a central server, the AI model is sent to the user's device, where it learns locally. Only the model’s insights or updates (and not the actual data) are sent back to the central server. This makes it possible to train smarter models without compromising user privacy. How do you see Federated Learning shaping the future of AI? Let’s discuss. #FederatedLearning #PrivacyFirst #AI #MachineLearning #DataPrivacy #SmartAI #EdgeComputing