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Homomorphic encryption is a tool that enables computation on encrypted data and thus has applications in privacy-preserving cloud computing. Though conceptually amazing, implementation of homomorphic encryption is very challenging and typically software implementations on general purpose computers are extremely slow. In this paper we present our domain specific architecture in a heterogeneous Arm+FPGA platform to accelerate homomorphic computing on encrypted data. We design a custom co-processor for the computationally expensive operations of the well-known Fan-Vercauteren (FV) homomorphic encryption scheme on the FPGA, and make the Arm processor a server for executing different homomorphic applications in the cloud, using this FPGA-based co-processor. We use the most recent arithmetic and algorithmic optimization techniques and perform design-space exploration on different levels of the implementation hierarchy. In particular we apply circuit-level and block-level pipeline strategies to boost the clock frequency and increase the throughput respectively. To reduce computation latency, we use parallel processing at all levels. Starting from the highly optimized building blocks, we gradually build our multicore multi-processor architecture for computing. We implemented and tested our optimized domain specific programmable architecture on Xilinx Zynq UltraScale+ MPSoC ZCU102 Evaluation Kit. At 200 MHz FPGAclock, our implementation achieves over 13x speedup with respect to a highly optimized software implementation of the FV homomorphic encryption scheme on an Intel i5 processor running at 1.8 GHz.

Localization based on premeasured WiFi fingerprints is a popular method for indoor localization where satellite based positioning systems are unavailable. In these systems, privacy of the user's location is lost because the location is computed by the service provider. In INFOCOM'14, Li et al. presented PriWFL, a WiFi fingerprint localization system based on additively homomorphic Paillier encryption, that was claimed to protect both the users' location privacy and the service provider's database privacy. In this paper, we demonstrate a severe weakness in PriWFL that allows an attacker to compromise the service provider's database under a realistic attack model and also identify certain other problems in PriWFL that decrease its localization accuracy. Hence, we show that PriWFL does not solve the privacy problems of WiFi fingerprint localization. We also explore different solutions to implement secure privacypreserving WiFi fingerprint localization and propose two schemes based on Paillier encryption which do not suffer from the weakness of PriWFL and offer the same localization accuracy as the privacy-violating schemes.

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The power of side-channel leakage attacks on cryptographic implementations is evident. Today's practical defenses are typically attack-specific countermeasures against certain classes of side-channel attacks. The demand for a more general solution has given rise to the recent theoretical research that aims to build provably leakage-resilient cryptography. This direction is, however, very new and still largely lacks practitioners' evaluation with regard to both efficiency and practical security. A recent approach, One-Time Programs (OTPs), proposes using Yao's Garbled Circuit (GC) and very simple tamper-proof hardware to securely implement oblivious transfer, to guarantee leakage resilience. Our main contributions are (i) a generic architecture for using GC/ OTP modularly, and (ii) hardware implementation and efficiency analysis of GC/OTP evaluation. We implemented two FPGA-based prototypes: a system-on-a-programmable-chip with access to hardware crypto accelerator (suitable for smartcards and future smartphones), and a stand-alone hardware implementation (suitable for ASIC design). We chose AES as a representative complex function for implementation and measurements. As a result of this work, we are able to understand, evaluate and improve the practicality of employing GC/OTP as a leakageresistance approach.