WorksheetsIoT Privacy, Security, and Governance Quiz
Total questions: 25
Worksheet time: 8mins
Which factor makes end-to-end security in IoT systems particularly challenging?
High device cost
Uniform communication standards
Device heterogeneity
High computational capacity
Why are traditional encryption algorithms like RSA or AES often unsuitable for IoT devices?
They are insecure
They consume too much computation and energy
They are outdated
They don’t support wireless transmission
What principle ensures that IoT devices only collect data strictly necessary for their function?
Data redundancy
Data minimization
Data extension
Data enrichment
Which IoT layer is most vulnerable to physical tampering and identity spoofing?
Network layer
Application layer
Perception layer
Cloud layer
Privacy-preserving data collection mainly focuses on:
Encrypting packets
Avoiding unnecessary data capture
Improving transmission speed
Maximizing sensor output
What is a key characteristic distinguishing IoT governance from traditional IT governance?
It excludes policy compliance
It involves distributed and autonomous devices
It is limited to cloud data
It uses centralized management only
A major governance issue in smart cities with multiple IoT domains is:
Lack of device availability
Interoperability and data ownership conflicts
Uniform privacy regulations
High data redundancy
Accountability in IoT governance primarily refers to:
Encrypting all data
Ensuring traceability and responsibility for actions
Automating access control
Hiding user data
Which organization provides international IoT governance and security standards?
NASA
ISO/IEC
W3C
IEEE-123
Which regulation most strongly influences IoT data privacy governance in Europe?
HIPAA
GDPR
COPPA
PCI-DSS
What was the main aim of FP7 (EU Framework Programme 7) regarding IoT?
Develop consumer gadgets
Fund research to strengthen IoT interoperability and trust
Build commercial IoT networks
Replace IPv4 with IPv6
Which FP7 focus area contributed significantly to building trust in IoT systems?
AI-based automation
Security and interoperability research
Social networking
Cloud gaming
Which FP7 project specifically targeted IoT privacy and data protection?
FI-WARE
Hadoop
DeepMind
EtherCAT
FP7 research recommended which of the following for secure IoT communication?
Plain HTTP
Layered security architecture
Public Wi-Fi networks
Centralized data hubs only
In smart city IoT platforms, balancing *data utility* and *privacy* often requires:
Raw data sharing
Data anonymization
Data duplication
Removing all metadata
A common mechanism to ensure *trustworthiness* of IoT data sources is:
MAC filtering
Reputation-based systems
Manual verification
Random selection
Context-aware security in IoT systems helps by:
Ignoring device context
Adapting security policies dynamically
Reducing encryption complexity
Fixing static security levels
Blockchain in smart cities enhances IoT trust by:
Centralizing data
Offering immutable and transparent data records
Encrypting all transactions
Increasing network latency
The “First Steps Towards a Secure Platform” approach focuses primarily on:
Security testing after deployment
Integrating security from the design phase
Ignoring firmware updates
Delaying access control
The SMARTIE project’s main objective was to:
Improve IoT hardware performance
Develop privacy and trust frameworks for smart cities
Build IoT entertainment systems
Create large IoT databases
How does SMARTIE manage privacy policies across multiple IoT domains?
Fixed access control lists
User-controlled and dynamic policy management
Centralized static rules
Default manufacturer settings
In IoT, data aggregation mainly helps to:
Increase bandwidth usage
Reduce data volume and transmission cost
Eliminate encryption
Slow down analytics
What is a major security challenge in aggregated IoT data?
Signal loss
Inference and reconstruction attacks
Device overloading
Low storage capacity
Which technique allows IoT platforms to perform computations on encrypted data without decryption?
Symmetric encryption
Homomorphic encryption
Public hashing
Compression
An ideal IoT data aggregation model for smart cities should:
Prioritize scalability, latency, and security balance
Focus only on latency
Exclude encryption to save power
Centralize all data in a single hub
