automatski - the internet of things - privacy in iot
TRANSCRIPT
PRIVACY IN IOTThe Internet of Things – Automatski Corp.
http://www.automatski.comE: [email protected] , Founder & CEO
M:+91-9986574181
E: [email protected] , Director - Sales
M: +91-8884074204
WHAT IS IDENTITY?
1. Name
2. Geographic categories smaller than a state
3. Dates (except for the year) that is related to an individual. This may include data of birth, death, admission, discharge, etc.
4. Telephone numbers
5. Fax number
6. E-mail address
7. Social security number (SSN)
8. Medical record number
9. Health plan beneficiary number
10. Account numbers
11. Certificate or license numbers
12. Vehicle identifiers (e.g. serial numbers, license plates)
13. Device identifiers
14. Web URLs
15. IP addresses
16. Biometric identifiers (e.g. finger print, voice print)
17. Full-face photos
18. Any other unique identifying number, code or characteristic
THE BIGGEST PROBLEM
Is NOT Direct Identification Because its relatively easy to De-Identify records
But rather Re-Identification! Aka Identity Reconstruction! Or Trail Reconstruction!
THE PROBLEM OF RE-IDENTITIFICATION
Latanya Sweeney’s 2006 research in which 87% of people in the United States can be identified by combining their ZIP code, birth date and sex.
Sweeney’s research also found that other types of information can also re-identify people.
For instance, 53% of US citizens can be identified by their city, birth date and sex
While 18% of citizens can be identified by their county, birth date and sex.
ADDRESSING RE-IDENTIFICATION
1. Access control: This is the traditional model for safeguarding individuals’ privacy. It is also referred to as query restriction, which associates certain data to a given request in a multi-level relational database.
2. Statistical disclosure control: This method includes a wide variety of techniques, including suppression, noise addition, perturbing records of a collection. Statistical disclosure control prevents the receiver of the data from inferring identities of the individuals.
3. Computational disclosure control: This model prevents the formation of direct connections from unidentified data to identifiable data. With computational disclosure control, records appear identical through generalization and suppression of attributes.
4. Algorithms: This model has been promoted most by the data mining industry to preserve the privacy of individuals.
ISSUES
Data De-Identification
Data Minimization
Degrees of Identification
De-Identification & Re-Identificiation
DE-IDENTIFICATION TECHNIQUES
Anonymization
Blurring
Disclosure Avoidance
Disclosure Limitation
Masking
Perturbation
Record Code
Redaction
Suppression
LETS BE CLEAR
We are NOT hiding from THE SYSTEM(S)
We are Hiding from the Unwanted Stalkers and Criminals who have access or can gain access to The System(s)
We are Hiding Digital Identity from the Real World Identity
We are Hiding Digital “Activities” from the Real World Identity
A FAMOUS APHORISM OF DAVID WHEELER
"All problems in computer science can be solved by another level of indirection"
THE SOLUTION
We are NOT hiding from THE SYSTEM(S)
We are Hiding from the Unwanted Stalkers and Criminals who have access or can gain access to The System(s)
We are Hiding Digital Identity from the Real World Identity
We are Hiding Digital “Activities” from the Real World Identity
Problem of Access & Disclosure
Problem of In-direction
Problem of In-direction
THANKYOU!
WHO ARE WE?
10-20+ years of Software Engineering experience each
Global Agile & Technology Consulting, Advisory & Delivery experience of 10-15+ years since Agile and Tech was in Infancy.
The first computers we worked on were Atari and ZX Spectrum ;-) And yes after Basic we went to C/C++ and then straight to Assembly Programming and then -> we began our journey as technologists
Globally Distributed Global & Fortune Company work Experience
Worked with companies like BCG, McKinsey, Fidelity, Tesco, Goldman Sachs…
Long 3-5+ year projects & Over 200+ people globally distributed teams
Led Double Digit Multi-Billion US$ Projects
Blended methodology used comprising of Scrum, XP, Lean and Kanban
From there we rode every wave J2EE, RUP, Six Sigma, CMMI, SIP, Mobile, Cloud, Big Data, Data Science etc…
Individually worked with over 300+ Technologies at a time, literally nothing that scares us
Authors, Speakers, Coach’s, Mentors, Scientists, Engineers, Technologists, Marketing, Sales, HR, Finance…
We are Generalists and we Always start with First Principles.
FURTHER INFORMATION
Please refer to http://automatski.com for more information
Please go through the 2 minute demo, 5 minute demo…
And the showcase section of the website for more information…
Or email us on [email protected]
Or just give us a shout on Linkedin, Facebook, Twitter, Email etc.