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Internship: Security for Distributed Machine Learning F/M Job

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Date: Oct 9, 2020

City: Mougins, 06, FR

Company: SAP

 

 

Requisition ID: 266525
Work Area: Information Technology
Expected Travel: 0 - 10%
Career Status: Student
Employment Type: Limited Full Time

 

COMPANY DESCRIPTION


SAP started in 1972 as a team of five colleagues with a desire to do something new. Together, they changed enterprise software and reinvented how business was done. Today, as a market leader in enterprise application software, we remain true to our roots. That’s why we engineer solutions to fuel innovation, foster equality and spread opportunity for our employees and customers across borders and cultures.

SAP values the entrepreneurial spirit, fostering creativity and building lasting relationships with our employees. We know that a diverse and inclusive workforce keeps us competitive and provides opportunities for all. We believe that together we can transform industries, grow economics, lift up societies and sustain our environment. Because it’s the best-run businesses that make the world run better and improve people’s lives.

 

ABOUT US (TEAM)

Maintaining security is a constantly shifting task, and we need to respond with continuous learning and research. The portfolio of SAP Security Research contains those topics that we believe are most important for SAP’s security future. 
SAP’s vision to secure business is built on 3 ideals: Zero-Vulnerability, to harden the software by eliminating vulnerabilities, Defensible Application, to enable the software to identify and prevent attacks, and Zero-Knowledge, to make any theft of data useless through encryption.
Considering these aspects, SAP Security Research covers the following focal areas: Anonymization for Big Data, Secure Internet of Things, Software security analysis, Open-source analysis, Deceptive application, Applied cryptography, Quantum technology, and Machine Learning as enabler for the next generation of security.


PURPOSE AND OBJECTIVES

This internship is based in the SAP Labs France Research Lab, in Sophia-Antipolis. The work will be performed in the context of the Research Program “Security & Trust”, and deals with secure integration of Internet of Things with SAP HANA applications. The Internet of Things (IoT) is expected to grow to 50 billion connected devices and $14.4 trillion in value at stake until 2020. SAP is exploiting this trend and centers its IoT development on the SAP HANA Cloud Platform IoT Service. 


Until now, the backend (on-prem & cloud) deployments were considered as the single source of truth & unique point of access in regards of Enterprise Systems (ES). Nevertheless, a paradigm shift has been recently observed, by the deployment of ES assets towards the Edge sectors of the landscapes; by distributing data, decentralizing applications, de-abstracting technology and integrating edge components seamlessly to the central backend systems. Capitalizing on recent advances on High Performance Computing along with the rising amounts of publicly available labeled data, Deep Neural Networks (DNN), as an implementation of AI, have and will revolutionize virtually every current application domain as well as enable novel ones like those on autonomous, predictive, resilient, self-managed, adaptive, and evolving applications. 


Distributively deployed AI capabilities will thrust the above-mentioned transition. As reported by Deloitte, “... companies are incorporating artificial intelligence in particular, machine learning into their ’Internet of Things applications’ and seeing capabilities grow, including improving operational efficiency and helping avoid unplanned downtime” [Schatsky et al., 2017].


The deployment of data processing capabilities throughout Distributed Enterprise Systems rises several security challenges related to the protection of input & output data [Parliament and Council, 2016] as well as of software assets. In the specific context of distributed intelligence, DNN based/enhanced software will represent key investments in infrastructure, skills and governance, as well as in the acquisition of data and talents. The software industry is therefore in the direct need to safeguard these strategic investments by enforcing the protection of this new form of Intellectual Property. Furthermore, on the wake of Data Protection (DP) regulations such as the EU-GDPR [Parliament and Council, 2016], Independent Software Vendors (ISVs) have the non-transferable requirement to comply with those. Therefore, ISVs aim to protect both: data and the Intellectual Property of their AI-based software assets, deployed on potentially unsecure edge hardware & platforms [Goodfellow, 2018].


The lack of solutions for IP protection exposes trained NN owners to reverse engineering on their DL models [Tramèr et al., 2016]. As outlined in [Augasta and Kathirvalavakumar, 2012] [Floares, 2008], attackers can steal trained NN models. In such new coding paradigm, where design patterns are enforced in known and legacy implementations, the question of IP is at stake. The question is not so much how to protect the DNN architecture (since most architectures are grounded on well-known research), but rather how to protect the trained DNN model.


Schatsky, D., Kumar, N., and Bumb, S. (2017). Intelligent IoT, Bringing the power of AI to the Internet of Things. Deloitte Insights.
Goodfellow, I. (2018). Security and privacy of machine learning. RSA Conference.
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., and Ristenpart, T. (2016). Stealing machine learning models via prediction apis. In USENIX Security Symposium, pages 601–618.
Augasta, M. G. and Kathirvalavakumar, T. (2012). Reverse engineering the neural networks for rule extraction in classification problems. Neural processing letters, 35(2):131–150.
Floares, A. G. (2008). A reverse engineering algorithm for neural networks. Neural Networks, 21(2-3):379–386.
Laurent Gomez, Marcus Wilhelm, José Márquez, Patrick Duverger, Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection, Secrypt‘19

 

EXPECTATIONS AND TASKS

In this internship, the student will:

  • Study state of the art on Security for Distributed Machine Learning;
  • Design of novel approach for AI-based software data protection and IP safeguarding;
  • Implementation of a Proof of Concept demonstrating the feasibility of such approach on an industrial use case.

We expect that 60% of time will be dedicated to development and 40% to research activities.

 

PROFILE/EDUCATION/SKILLS AND COMPETENCIES

  • University Level: Last year of MSc in Computer Science or beyond
  • C, Python, Solidity
  • Experience on Smart Contracts, Blockchain, Machine Learning, Cybersecurity
  • Fluency in English (working language)
  • Abilities in organizing meeting and contacting people
  • Good oral and written communication skills
  • Capacity to write documents in English, ability to synthesize

PROFESSIONAL EXPERIENCE

  • None required

 

WHAT YOU GET FROM US

Success is what you make it. At SAP, we help you make it your own. A career at SAP can open many doors for you. If you’re searching for a company that’s dedicated to your ideas and individual growth, recognizes you for your unique contributions, fills you with a strong sense of purpose, and provides a fun, flexible and inclusive work environment – apply now.

SAP'S DIVERSITY COMMITMENT
To harness the power of innovation, SAP invests in the development of its diverse employees. We aspire to leverage the qualities and appreciate the unique competencies that each person brings to the company.

SAP is committed to the principles of Equal Employment Opportunity and to providing reasonable accommodations to applicants with physical and/or mental disabilities. If you are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e-mail with your request to Recruiting Operations Team (Americas: Careers.NorthAmerica@sap.com or Careers.LatinAmerica@sap.com, APJ: Careers.APJ@sap.com, EMEA: Careers@sap.com).

Successful candidates might be required to undergo a background verification with an external vendor.

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