of Things the Smart Campus, and Blockchain · Internet of Things (IoT), the Smart Campus, and Blockchain Nicole Radziwill ... residents of the EU should not be deprived of security

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Internet of Things (IoT), the Smart Campus, and Blockchain

Nicole RadziwillAssociate Professor, Department of Integrated Science & Technology (ISAT)


• About me• Technical topics:

1. Industry 4.0 & IIoT2. IoT & Smart Cities3. Participatory Sensing4. Blockchain5. GDPR

• Practical Recommendations/Q&A



From Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems.

Quality 4.0: Leveraging Connected, Intelligent, Automated Systems for:• Predictive maintenance• Predictive asset management• Assess supply chain status & risk in real time• Analysis of images and other data to assess conformity and compliance• Reducing losses associated with manufacturing processes


1) Industry 4.0 & the Industrial Internet of Things (IIoT)


From Hazen, T. R. (n.d.) Historically: How to Site a Mill. Retrieved from http://www.angelfire.com/journal/millrestoration/site.html

http://www.ledyardsawmill.org/history/early‐sawmills‐in‐new‐england


The “Industrial Revolutions”

From Intelligente Lösungen für die Wissensgesellschaft (German Research Center for Artificial Intelligence) at http://www.dfki.de

• Industry 1.0: Water & Steam Power

• Industry 2.0: Electricity, Mass Production, Division of Labor

• Industry 3.0: Automated production systems powered by PLCs

• Industry 4.0: New innovations catalyzed by Cyber‐Physical Systems



From Andres, R. (2015). Leveraging Computational Power at the Edge: IoT/M2M Solutions with Informix in the Internet Gateway. Available from https://www.slideshare.net/Eurotechchannel/iot‐m2m‐solutions‐with‐informix‐in‐the‐iot‐gateway

Information Technology (IT):1. Confidentiality2. Integrity3. Availability

Operations Technology (OT):1. Availability2. Integrity3. Confidentiality

• Long lifecycles (10‐40 years)• Security historically not a priority

• Short lifecycles (2‐5 years)• Security has been a priority


2) Internet of Things (IoT) Smart Cities

Optional School Logo HereFrom https://www.getcujo.com/internet‐of‐things‐security‐device‐cujo/


Edges you controlEdges you don’t control

20.8B endpoints by 2020 (Gartner)

AI/ML

AI/ML



IoT is the “Glue” for Smart Cities

From Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted to know about smart cities: The Internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60‐70.

• Connected• Intelligent• Automated


Why Smart Cities?Potential benefits include being able to:

• Manage infrastructure investments & plan for new investments more effectively• Provide more efficient, new, or enhanced services for citizens• Reduce organizational silos and create new levels of cross‐sector collaboration• Assist local and national governments towards meeting large‐scale goals like addressing climate change

• Enable innovative business models and platforms

From Holler, J., Tsiatsis, V., Mulligan, C., Avesand, S., Karnouskos, S., & Boyle, D. (2014). From Machine‐to‐machine to the Internet of Things: Introduction to a New Age of Intelligence. Academic Press. P. 283.


A Smart Campus is a Smart City!• Facilities

• Enhance sustainability practices (energy mgmt, lighting, HVAC, dripping faucets, fountains)• Facilitate better asset management (through predictive maintenance)• Fleet management, automated maintenance and cleaning

• Student Life• Improve safety• Improve student welfare

• Student Learning• Improve communications• Provide access to educational resources

• “Community Experience”• Parking & game day engagement• Personalized campus experience



Insights Powered by Aggregating Lots of Data

Adapted from Schleicher, J. M., Vögler, M., Inzinger, C., & Dustdar, S. (2015, October). Towards the internet of cities: a research roadmap for next‐generation smart cities. In Proc. ACM First International Workshop on Understanding the City with Urban Informatics (pp. 3‐6). ACM.

Smart Homes/ Apartments/ Dorm Rooms

Smart Building Automation Systems (BAS)

Participatory Sensing


What Can Go Wrong?• Energy theft• Asset Damage/Destruction• Systematized Biases• Stalking• Road Rage Hacking• Racial Profiling• Unwanted Data Aggregation• “Right to be Forgotten”• “Privacy of the Commons” – when one person’s voluntary disclosure inadvertently exposes the personal information of others

Video: https://www.youtube.com/watch?v=‐RYCXDUt2m8 (1:42)


3) Participatory Sensing


Participatory Sensing

From Christin, D., Reinhardt, A., Kanhere, S. S., & Hollick, M. (2011). A survey on privacy in mobile participatory sensing applications. Journal of systems and software, 84(11), 1928‐1946.


Privacy and Threats to PS“Without any suitable protection mechanism however, mobile phones are transformed into miniature spies, possibly revealing private information about their owners.”

• Video or audio recordings of intimate discussions or activities• Keeping track of places a user has been or people they have been with• Revealing or disclosing habits or habit patterns

There is no common definition for privacy shared among developers in this sector. Christin et al. recommend:



Context of Information Sharing“Socio‐cultural and contextual differences have a strong impact on the individual perception of data sensitivity.” For example,

• Sharing location data with a single trusted person is common• Sharing location data more generally depends on an individual’s willingness to attract attention and promote themselves

• Sharing any information will depend upon locational context (e.g. home, office, school, family gatherings, sports or cultural events, political events)

• Conditions and extent of data sharing will also depend on:• Who gathers the sensor readings?• Who analyzes (and aggregates) the sensor readings?• Who accesses the analyzed sensor readings?



Hidden Threats• Time and location – even information from photos and videos posted on social media (points of interest, lighting, noise) can reveal home and workplace locations, routines, habits, and political views

• Acceleration – can reveal clues about an individual’s unique gait, and thus identity

• Environmental data – can be used to determine an individual’s location in places where GPS data may be inaccurate, e.g. in buildings

• Biometric data – can unwittingly communicate information about disease, even when used to access buildings

We often easily and willingly give up privacy to gain access to features that we want to use.


CountermeasuresArchitectural elements can be designed into PS applications to enhance security and privacy:

• Tailored sensing and user preferences – add sensitivity to context (home, office, event) and control sampling frequency (1x/second, 1x/minute, 1x/hour, 1x/day)

• Anonymous task distribution – randomly sample sensors, use “tasking beacons”, use routing schemes designed to protect location privacy

• Anonymous data reporting – send reports without identifying source• Pseudonymity – allow users to use alternate identities when reporting• Data perturbation – add artificial noise to protect identity & location• Hiding sensitive locations – when a user encounters a sensitive location, the application generates fictitious (but reasonable) locations and attaches them to the observation

• Data aggregation – combine results from multiple sensors to smooth noise


4) Blockchain


Blockchain Data Structure

From https://www.slideshare.net/IBMDevOpsforEnterpriseSystems/making‐blockchain‐real‐for‐business‐at‐the‐z‐systems‐agile‐enterprise‐development‐conference‐2016


Hash a Phrase or Password> phrase <- "Here is my password"

> digest(phrase, "sha256")"6fc0926f03bc8a445fb498bd9d7adb017a3c57b95073f72fd25e8c04190ef2e5“

> digest(phrase, "sha512")"b0474e7cbb8e6a0847b47df20393f464c91c7825c358684eb2d4c87bb330dcb60b3a95dee25b197e839934e1271825320b5cde05ea8e66246f1fee04275c3d30"


Hash Plato> book <- gutenberg_download(150)

> digest(book, "sha256")"6adc59638784ec62646f16b74f651315e583666af9fefbe7d7f0b95a1ae998b8"


Hash My Cat> kitty <- readJPEG("kitty.jpg")> digest(kitty, "sha256")"dcd239ba6a09080eb61b7310a5428753f63d05ae2b282bf81dc0182f7552f60d"


Hash My Cat

1 2

3 4


Hash My Cat> digest(kitty, "sha256")[1] "dcd239ba6a09080eb61b7310a5428753f63d05ae2b282bf81dc0182f7552f60d"

> digest(kitty2, "sha256")[1] "dcd239ba6a09080eb61b7310a5428753f63d05ae2b282bf81dc0182f7552f60d"

> digest(kitty3, "sha256")[1] "fe5791ee490693d7d7b25379278b2374c3afda25c76aec5f3aa17e7e8b184362"

> digest(kitty4, "sha256")[1] "dcd239ba6a09080eb61b7310a5428753f63d05ae2b282bf81dc0182f7552f60d"


Blockchain Amplifies Tampering:Small change in an object Large change in a hash

Blockchain “broken”


Blockchain Provides…• Immutable record of peer‐to‐peer transactions

• Transparent recordkeeping• Immediately auditable• Relief from the need for a central, managing authority

• Private channels to protect data privacy (“permissioned” blockchains)

Need to identify:

• Participants• Assets• Transactions• Conditions for Transactions

(“Smart Contracts”)


Roles for Auditors

• Quality Assurance for Smart Contracts ‐make sure right factors are in place• Blockchain Architect ‐ ensure compliance between design and intentions• Blockchain Administrator – add users, grant permissions, add new business processes • Arbitrator – resolve disputes

Contracts are still agreements between multiple entitiesand require INTERPRETATION

From https://iaonline.theiia.org/blogs/Jim‐Pelletier/2018/Pages/A‐Blockchain‐Primer‐for‐Internal‐Audit.aspx


IoT + Blockchain• Reduce IoT operations costs by eliminating intermediaries• A “security sentinel” to manage software and firmware updates to IoT devices more autonomously

• Allow machines to exchange information with each other, possibly at a cryptocurrency price (enabling new business models)

• Facilitate troubleshooting by providing a high quality record of the messages sent between machines


5) GDPR


GDPR (General Data Protection Regulation)“The GDPR can in many cases apply to U.S. higher education institutions if those institutions control or process data about residents of the EU. Unlike prior laws, the GDPR takes the position that residents of the EU should not be deprived of security and privacy protections solely because a business or organization that targets those residents is located elsewhere.” ‐‐https://er.educause.edu/blogs/2017/8/gdpr‐a‐data‐regulation‐to‐watch

• The cornerstone of GDPR is consent• Applies to any organization collecting or processing data on behalf of EU residents• Established April 2016 but will be enforced beginning May 26, 2018• Places legal obligations onto those who control and process:

• Personal data – email, physical address, online identifiers (e.g. home IP addresses)• Sensitive personal data – health/genetic information, biometrics

* AlienVault


GDPR and BlockchainPotential threats:

• Public, permissionless blockchains are the antithesis of GDPR!• Smart contracts making decisions on your behalf when GDPR asks that citizens be informed of all decisions

• Enforcing the “right to be forgotten”

From Moeller et al. (2018) An overview of blockchain technology and the GDPR. Available at https://static1.squarespace.com/static/59cb821618b27d9277121e21/t/59d37c7a51a584ef26bab480/1507032187643/BAI_DOC.pdfhttps://static1.squarespace.com/static/59cb821618b27d9277121e21/t/59d37c7a51a584ef26bab480/1507032187643/BAI_DOC.pdf

Piekarska et al. (2018) When GDPR becomes real, and blockchain is no longer fairy dust. Available at https://docs.google.com/document/d/1wnRYOZrGZS6h_‐PnJRaq1pAc35GrfGUZcXh46sTHdFQ/edit#heading=h.xlkpssu5kfbq


6) What do we do?


Build Resilience“In many situations the potential for accidental or intentional subversion of security by a person will be the system’s weakest link.” Quality systems like the NIST Cybersecurity Framework (below) and the Malcolm Baldrige process can guide organizations as they build resilience – in particular, by emphasizing social capital and trust relationships.

Quote from Boyce, M. W., Duma, K. M., Hettinger, L. J., Malone, T. B., Wilson, D. P., & Lockett‐Reynolds, J. (2011, September). Humanperformance in cybersecurity: a research agenda. In Proc. Human Factors and Ergonomics Society annual meeting (Vol. 55, No. 1, pp. 1115‐1119).


Other Practical Recommendations• Engage in rigorous asset management – identify IoT endpoints

• Document use cases and abuse cases• Identify main types of IoT devices and sensors, what each does to collect/ store/ communicate data, main risks and benefits of each transaction

• Move towards continuous risk assessment practices• Invest in cybersecurity management and governance to build resilience –if “nothing is happening” that means your staff is successful!

• Enhance capabilities, reduce vulnerabilities, build social capital

Early NIST guidance on IoT risk management: https://www.nist.gov/sites/default/files/documents/2018/04/13/iot_program_discussion_draft_april_2018.pdf


[email protected]


Supplemental Slides


CountermeasuresArchitectural elements can be designed into PS applications to enhance security and privacy:



• Anonymous data reporting – send reports without identifying source• Pseudonymity – allow users to use alternate identities when reporting• Data perturbation – add artificial noise to protect identity & location• Hiding sensitive locations – when a user encounters a sensitive location, the application generates fictitious (but reasonable) locations and attaches them to the observation

• Data aggregation – combine results from multiple sensors to smooth noise


Countermeasures (1)Architectural elements can be designed into PS applications to enhance security and privacy:



• Anonymous data reporting – send reports without identifying source• Pseudonymity – allow users to use alternate identities when reporting• Spatial cloaking – use tessellation or other algorithms to obscure the location from which the reporting is being done


Countermeasures (2)• Data perturbation – add artificial noise to protect identity & location• Hiding sensitive locations – when a user encounters a sensitive location, the application generates fictitious (but reasonable) locations and attaches them to the observation

• Data aggregation – combine results from multiple sensors to smooth noise• Privacy‐aware data processing – combine observations into descriptive statistics or maps which are not associated with only one observer

• Review, deletion, storage, and retention of data – locally store data on phone until/unless user grants permission for storage in a central repository

• Access control and audit – allow users to control the types of people (researchers, medical staff, friends, family, neighbors) who can access their information


Learning = models continuously updated, performance continually improves

People Intelligent Systems

As people learn about the work environment, they update: • policies, • procedures, • practices, and • heuristics

to improve the performance of people, projects, processes &

the bottom line.

When AI/ML systems learn, they update how they:

• predict, • classify, • find patterns, and identify

important variables.



How to Build Resilience• Awareness – Accurate, reinforced risk communication to the public, through professional societies, journalists, science museums and centers; holding seminars and news conferences

• Leadership – Being able to “galvanize emergency operations” despite damages and loss; the “most critical and least predictable”

• Resource Allocation – Drills and emergency response exercises• Planning – A view towards long‐term investments to ensure robustness, redundancy, resourcefulness, & rapidity

From O'Rourke, T. D. (2007). Critical infrastructure, interdependencies, and resilience. BRIDGE‐WASHINGTON‐NATIONAL ACADEMY OF ENGINEERING‐, 37(1), 22. Available from https://pdfs.semanticscholar.org/6c17/b35ec7555a9f27d5ccb6ca1d357a20b5ce0a.pdf


Building Resilience through Social Capital

• Effective leadership (one characteristic of resilience) requires effective followership, and the requirement of rapidity suggests that people cooperating during a crisis must be able to configure and reconfigure themselves as circumstances dictate.

• According to Aldrich, who studied resilience in Japan after the Kobe (1/17/1995) and Sendai (3/11/2011) earthquakes, research and policy emphasize building resilience in physical infrastructure, but social capital is a much greater determinant of ability to quickly recover.

From Aldrich, D. P. (2012). Building resilience: Social capital in post‐disaster recovery. University of Chicago Press.

Daniel Aldrich’s Twitter profile picture.

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of Things the Smart Campus, and Blockchain · Internet of Things (IoT), the Smart Campus, and Blockchain Nicole Radziwill ... residents of the EU should not be deprived of security