Revisiting the Privacy Needs of Real-World Applicable ... · 2 Jan Pennekamp [email protected] Vision of an Internet of Production (IoP) Federal-funded research cluster

https://comsys.rwth-aachen.de/

Revisiting the Privacy Needs of Real-World Applicable Company

Benchmarking

Jan Pennekamp, Patrick Sapel, Ina Berenice Fink, Simon Wagner, Sebastian Reuter, Christian Hopmann, Klaus Wehrle, and Martin Henze

Virtual Event / WAHC 2020, 15th December 2020

2 Jan [email protected]

Vision of an Internet of Production (IoP)

� Federal-funded research cluster in Aachen, Germany� Over 35 institutes in Aachen, ~ 50 Mio € in funding

� Goal is to create a “World Wide Lab”� To utilize data from production, development and usage

� In real time (adaptively) with an adequate level of granularity

� Even in cross-domain collaboration

2 Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC-2023 Internet of Production – 390621612

How to identify unrealized potentials in industrial settings?


An Introduction to Company Benchmarking

2

4

6

8

10

Overall Company Performance

Finance

Efficiency of the manufacturing equipment

Efficiency of theproduct range

Efficiency of themanufacturing processes

Staff

Quality andcustomer

satisfaction

Maximum Company Average 10 = good; 1 = bad

� Measure performance� Based on KPIs

� Includes several aspects

¾Business perspective¾Operational practices¾…

� Identify potentials� Adapt own business

processes to catch up

� Utility improves with the number of participants

� Advance overall state (in production)


Scenario: Stakeholders in Benchmarking Services

Company n

Company 1

Benchmark Service

Analyst

Participate in Benchmarking

ProvidesAlgorithm

Storage Statistics

ComputesBenchmark

Require a solution that (a) addresses the concerns of allparticipants and the analyst and (b) is ready for today’s use

Access to Data (Company Inputs)

Access to Algorithm(Analyst’s Property)

Keep sensitive data private

Protect the algorithm


Data Challenges for Benchmarking Platforms

ComputedKPI xyz [%]

Best in ClassWorst in Class AverageOwn

95.337.1 71.657.4

Output/Result

� Compare to and learn from other “similar” companies

• Company Privacy• Raw data is sensitive• Fear of data leaks & a loss

of competitive advantage• “Own” result should be

known exclusively

• Exactness• Identified potential

should be accurate• Participants pay to get

access to the results • Disallows abstraction

Company privacy & exactness define the utility of a benchmarkand encourage companies to participate


Platform-specific Challenges

• Complexity• KPIs are based on

complex computations• Different operators and

inputs required

• Algo. Confidentiality• Lots of effort invested in

constructing the algo.• Competitive advantage

of the analyst

• Flexibility• Easy to deploy & use• Companies participate

at own discretion• Future-proof

Essential for real-world applicability

Not considered by related work!

Not considered by related work!


Cryptographic Primitives and Related Work

� Several approaches considered benchmarking settings� However: Focus on private comparison of KPIs, not their derivation� Thus, real-world value is questionable (also due to used primitives)

� Several cryptographic primitives are available� Secure Multi-Party Computation

¾Round-based approaches contradict flexibility¾Proposed approaches leak the algorithm or do not consider its sensitivity!¾Overhead in terms of computation and communication?

� Homomorphic Encryption

� Zero Knowledge Proofs (?)

� …

Related work is insufficient for industrial settings, andcentralized (plaintext) solutions are not an option either!


Shares Enc.Aggregates

Statistics Server

PCB: Privacy-preserving Company Benchmarking

Company n

Requests Enc. Inputs& Calculations

Provides Enc. Inputs & Results

ProvidesAlgorithm

Company 1

Analyst

Privacy Proxy

Provides KPI Statistics

Encrypted Storage

ComputesBenchmark

∑

1

2

3

4

OffloadsComplex

CalculationsB

5

LocalCalculationsA

Operates

6

Protects the algorithm!

Access to aggregates only!Own KPIs only locally known

(Raw) Data is encrypted

Complete independenceof participants

Flexibility introducedthrough offloading

Usability eased withweb-based client

Unique key per Company

Statistic Server’s key


Realization of PCB

� Offloading & algorithm confidentiality� Operations not supported by HE are computed by the participants

� Tunable obfuscation of the algorithm using

¾Randomization of requests (and computation)¾Dummy requests (discarded afterward)¾Blinding of computations (supported even without private HE key)

� Web-based client for usability� Requiring a web browser only

� No other dependencies (implements calculations and HE in WebAssembly)

� Participants only need to interact with the browser, no challenging setup required

Privacy Proxy


Implementation & Evaluation of PCB

� Python prototype with an additional WebAssembly client� Microsoft SEAL with FHE CKKS scheme

� For support of floating point numbers

� Configured 128 bit-level security (polynomial modulus of 16,384)

� Our prototype identifies extrema with OPE for simplicity� A more secure HE-based approach should be used in the real world

Single Operations1

Nested Computations2

Real-World Evaluation3

30 runs99% CI

Intel i5-2410M4GB RAM & HDD

no network constrains


Single Operations & Nested Computations1 2

No network effects captured!HE overhead as expected

Linear scalability for long chains is acceptable

1

2


Real-World Company Benchmark

� Domain of injection molding� Companies answered paper questionnaires

� Presentation of results individually by analyst

� Conducted in 2014

� 48 KPIs derived� 674 inputs� 2173 operations

� 1429 locally

� 744 offloaded� 15 layers of formula

3


Real-World Performance Results3

Aggregation is negligible

Observed runtime and traffic are real-world feasible

8.7 min 6.7 GB

Exactness

Network

Complexity


� Real-world benchmarks must considercompany privacy and algorithm confidentiality� Related work previously neglected the algorithm’s value!

� To address these needs,we propose our HE-basedbenchmark service PCB

� Our evaluation based on a real-world benchmark underlines its applicability in today’s industrial settings� Thus, we enable companies to identify potentials with a ready design

Conclusion: PCB – A Privacy-Preserving Benchmark

Thank you for your attention!

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Statistics Server

Analyst

Privacy Proxy

∑

1

2

3

4

B

5

A

Documents

Revisiting the Privacy Needs of Real-World Applicable ... · 2 Jan Pennekamp [email protected] Vision of an Internet of Production (IoP) Federal-funded research cluster