automatski - the internet of things - privacy in iot

PRIVACY IN IOTThe Internet of Things – Automatski Corp.

http://www.automatski.comE: Aditya@automatski.com , Founder & CEO

M:+91-9986574181

E: Shubhadeep.dev@automatski.com , 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 aditya@automatski.com

Or just give us a shout on Linkedin, Facebook, Twitter, Email etc.