Lightweight, Scalable and Secure Computations for Engineering Design

Siva C. Chaduvula, Mikhail J. Atallah, Jitesh H. PanchalPurdue University

RQ: How can designers execute their computations securely and efficiently in a co-design setting?10

.7 96.3

953.7

3.95 29.7

396.6

1 0 100 1000VECTORLENGTH

BANDWIDTH(KB)Sharemind ProposedApproach

Computational overhead imposed by existing cryptographic approaches make them unsuitable for

intensive and iterative engineering computationsInformation shared is prone to leakage and misuse

through collaborators

2. Privacy Preserving Elicitation of Customer Needs

References1. Chaduvula SC, Atallah MJ, Panchal JH. Secure Co-design: Achieving Optimality without Revealing. Journal of Computing and Information Science in Engineering, 2018, In Press, doi:

10.1115/1.40394312. Wang, S., Bhandari, S., Chaduvula, S.C., Atallah, M.J., Panchal, J.H. and Ramani, K., 2017. Secure collaboration in engineering systems design. Journal of Computing and Information Science

in Engineering, 17(4), p.041010. 3. Sharemind: Bogdanov, D., Niitsoo, M., Toft, T. and Willemson, J., 2012. High-performance secure multi-party computation for data mining applications. International Journal of Information

Security, 11(6), pp.403-418.

Boeing Mitsubishi1. Security Mismatch

X

3. Common Collaborator

Y XBoeing Mitsubhishi

2. Future Competitor

X Boeing Airbus

Collaborator

Internal attacker

External attacker

General Electric

X X

Confidential Input : X

Note: Enterprises mentioned in this slide are purely for illustrative purposes

Source: Boeing Images: Image taken from http://wildabouttravel.boardingarea.com/2017/02/the-787-10/

Engineering collaborations involve sensitive information flow among designers from

different enterprises from multiple countries

Mot

ivat

ion

App

roac

h &

Res

ults

Bro

ader

Impa

cts 1. Secure Outsourcing 3. Secure eProcurement

Cloud service provider

Designer

Additive Share 1

AdditiveShare 2

Collaborator 1Confidential value : 11

11+1811

-1811

Collaborator 2

Third party

Fast Lightweight And SecureComputations (FLASC)

• Adding/multiplying a parameter with a largerandom number hides the parameter

• Adding or multiplying a large number is orders ofmagnitude faster than existing cryptographicprimitives that rely on modular exponentiation

SPOs Performance

Scalability (VIP)

Secure Primitive Operations (SPOs)

Application Logic

Vector Inner product (VIP)

Less than Zero (LTZ)

Shuffle and re-split (SRS)

Equal to Zero(ETZ)

Direct Min (Dmin)

Matrix product (MP)

Linear Solver (LS)

Sort (S)

Set Operations Order statistics

Branching (B)

Matrix Inverse (MI)

Exponentiation (EXP)

Addition(ADD)

Eigenvector(EV)

Gradient Descent (GD) SVD (SVD)

Matrix Rank (MR)

Auctions (A)