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2025-12-10 Data Privacy (Fuad)

Definitions

  • Unauthorized instruction
    • Unauthorized network access
    • Unauthorized data access
    • Unauthorized surveillance
    • Unauthorized system behavior
  • Privacy vs security: Think like your data is gold,
    • Privacy - who has access?
    • Security - how to get access
  • I need to maintain my identity privately
  • I need to maintain security by verifying everyone’s identity.
  • Security goes around privacy.
  • Law makers: GDPR (Europe), CCPA (California, USA), and PIPEDA (Canada)
  • CS: Anonymization techniques and Differential privacy

Myths

  • Myth 1: My data is not personal, so does it count?
    • Used to justify metadata collection
    • Assumes only explicit identifiers matter
  • Attack:
  • loc3 = 2 > t0, loc3 (Trajectory 1), loc3 (Trajectory 2)

  • At night, people are less likely to move places which is why they got this formula:

  • They try to figure out the next move,

  • Myth 2: You have zero privacy anyway - get over it.
    • You can be captured on CCTV without consent, for example, or Covid 19 getting notification that the next person in your vicinity has the virus
  • The Europe General Data Protection Regulation (GDPR) has explicitly made Data Protection by Design and by Default an obligation for data controllers
    • Considered from the earliest design stages.
    • Preventing data from being collected for one specific purpose is easily reused for another purpose
    • Built-in privacy safeguards.
    • Privacy beyond purely technical components, addressing organizational procedures and business models.
  • Myth 3: “If you’re not doing anything wrong...”
  • Frames privacy as secrecy:
    • Only individuals engaging in wrongdoing need privacy, and if you have nothing to fear, you have nothing to hide
  • Normalizes surveillance
  • Right vs Wrong
    • The premise assumes that the ethical status of an action is easily answered: either you did something bad, or there is nothing to worry about.
    • The distinction between “right” and “wrong” is complex, depending on legal constraints, cultural context, and jurisdiction
    • Example: sexual orientation or smoking cannabis
    • The common assumption that only wrongdoing requires privacy is often perpetuated by those who try to exploit the data collected
  • Myth 4: “We Always Need to Know Who You Are.”
    • Assumes identity is required everywhere.
    • Assumes that accessing most online resources necessarily requires users to fully identify themselves (name, address, etc.)
    • Encourages unnecessary tracking
    • Do we need to give our fingerprints to access the gym?
  • Authentication is NOT identification
  • Systems can be designed to allow individuals to prove they meet a requirement without revealing their complete identity.
  • Anonymous and pseudonymous options exist.
  • For many services, it is sufficient to know whether a user has a specific attribute.
  • Myth 5: “Your Data Is Safe With Us.”
    • Companies overestimate their security
    • Large companies frame privacy as merely a security issue to be solved through centralized control.
    • Users cannot verify claims.
  • Reality: Data Is a Liability
  • Breaches are inevitable
    • Privacy is defined by the individual being in control of their data, not by trusting a massive, centralized entity.
  • Insiders and third-party vendors introduce risk
  • Not sharing location unless it is really needed.
  • Data not collected cannot be breached
  • Centralized vs Distributed
    • Decentralized or fully distributed peer-to-peer designs provide strong privacy by processing data locally on the user’s device.
  • Caveat: Although decentralization is powerful, techniques such as federated learning still enable the system as a whole to predict, evaluate, and nudge the user, even when personal data is processed locally.
  • Myth 6: “Privacy Competes With Innovation”
    • Frames privacy as a barrier
    • Used to justify over-collection
    • Privacy, utility, security, and functionality are opposing, irreconcilable goals, meaning that achieving one requires sacrificing the others.

Privacy Enhancing Techniques

  • K-Anonymity:
  • Differential Privacy:
  • Differential Privacy 2:

Future Challenges: Large Language Models

Large Language Models - attack strategies - Passive Leakage: Sensitive Inquiries - A PhD student accessed Samsung’s data, noting that it relies on a third-party company, and that the information he obtained was also private. This was a huge mistake made by Samsung. - Passive Leakage: Contextual Leakage - Mental and sensitive data should not be revealed with LLM. - Law is not the same as any authorized person, like the police, can have access to that data. - Passive Leakage: Personal Preferences Leakage - A big LLM will soon start using your data to create an advertisement, which will lead to your data being used. - Active Attacks: Jailbreak Attack - Yu, Zhiyuan, et al. “Don’t listen to me: Understanding and exploring jailbreak prompts of large language models.” 33rd USENIX Security Symposium (USENIX Security 24). 2024. - Active Attacks: Jailbreak Attack 2

Large Language Models

Attack Targets - Attack Target: Membership Inference Attacks - They need to know about previous data to learn more about you, - Attack Target: Model Inversion Takeaways - Lessons: - Privacy is a technical and ethical requirement - Tracking is embedded deeply in modern architectures - By now, you should be able to: - Explain why privacy matters in computing systems - Identify privacy risks from a technical and societal lens - Develop critical perspectives on tech design decisions

Food for Thought - Are ‘free’ services ethically acceptable? - Should CS professionals be liable for privacy harms? - What should students prioritize: innovation or safety? - Importance of privacy-by-design development

T-722-PRIV, Foundations of Data Privacy: A Legal and Technical Perspective [email protected]