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Chen, Huili, Cammarota, Rosario, Valencia, Felipe, Regazzoni, Francesco.  2019.  PlaidML-HE: Acceleration of Deep Learning Kernels to Compute on Encrypted Data. 2019 IEEE 37th International Conference on Computer Design (ICCD). :333—336.

Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., end-users, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about users' proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and Multi-Party Computation (MPC) into ML services to address privacy concerns from a technology standpoint. Existing PPML solutions have not been widely adopted in practice due to their assumed high overhead and integration difficulty within various ML front-end frameworks as well as hardware backends. In this work, we propose PlaidML-HE, the first end-toend HE compiler for PPML inference. Leveraging the capability of Domain-Specific Languages, PlaidML-HE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidML-HE on different ML kernels and demonstrate that PlaidML-HE greatly reduces the overhead of the HE primitive compared to the existing implementations.

Cammarota, Rosario, Banerjee, Indranil, Rosenberg, Ofer.  2018.  Machine Learning IP Protection. 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). :1—3.

Machine learning, specifically deep learning is becoming a key technology component in application domains such as identity management, finance, automotive, and healthcare, to name a few. Proprietary machine learning models - Machine Learning IP - are developed and deployed at the network edge, end devices and in the cloud, to maximize user experience. With the proliferation of applications embedding Machine Learning IPs, machine learning models and hyper-parameters become attractive to attackers, and require protection. Major players in the semiconductor industry provide mechanisms on device to protect the IP at rest and during execution from being copied, altered, reverse engineered, and abused by attackers. In this work we explore system security architecture mechanisms and their applications to Machine Learning IP protection.

Ray, Sandip, Chen, Wen, Cammarota, Rosario.  2018.  Protecting the Supply Chain for Automotives and IoTs. Proceedings of the 55th Annual Design Automation Conference. :89:1–89:4.
Modern automotive systems and IoT devices are designed through a highly complex, globalized, and potentially untrustworthy supply chain. Each player in this supply chain may (1) introduce sensitive information and data (collectively termed "assets") that must be protected from other players in the supply chain, and (2) have controlled access to assets introduced by other players. Furthermore, some players in the supply chain may be malicious. It is imperative to protect the device and any sensitive assets in it from being compromised or unknowingly disclosed by such entities. A key — and sometimes overlooked — component of security architecture of modern electronic systems entails managing security in the face of supply chain challenges. In this paper we discuss some security challenges in automotive and IoT systems arising from supply chain complexity, and the state of the practice in this area.