MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference

Secure Multiparty Computation from SGX

International audienceIsolated Execution Environments (IEE) offered by novel commodity hardware such as Intel's SGX deployed in Skylake processors permit executing software in a protected environment that shields it from a malicious operating system; it also permits a remote user to obtain strong interactive attestation guarantees on both the code running in an IEE and its input/output behaviour. In this paper we show how IEEs provide a new path to constructing general secure multiparty computation (MPC) protocols. Our protocol is intuitive and elegant: it uses code within an IEE to play the role of a trusted third party (TTP), and the attestation guarantees of SGX to bootstrap secure communications between participants and the TTP. In our protocol the load of communications and computations on participants only depends on the size of each party's inputs and outputs and is thus small and independent from the intricacy of the functionality to be computed. The remaining computational load-essentially that of computing the functionality-is moved to an untrusted party running an IEE-enabled machine, an appealing feature for Cloud-based scenarios. However, as often the case even with the simplest cryptographic protocols, we found that there is a large gap between this intuitively appealing solution and a protocol with rigorous security guarantees. We bridge this gap through a comprehensive set of results that include: i. a detailed construction of a protocol for secure computation for arbitrary functionalities; ii. formal security definitions for the security of the overall protocol and that of its components; and iii. a modular security analysis of our protocol that relies on a novel notion of labeled attested computation. We implemented and extensively evaluated our solution on SGX-enabled hardware, providing detailed measurements of our protocol as well as comparisons with software-only MPC solutions. Furthermore, we show the cost induced by using constant-time, i.e., timing side channel resilient, code in our implementation

Semi-Adaptively Secure Offline Witness Encryption from Puncturable Witness PRF

In this work, we introduce the notion of puncturable witness pseudorandom function (pWPRF) which is a stronger variant of WPRF proposed by Zhandry, TCC 2016. The punctured technique is similar to what we have seen for puncturable PRFs and is capable of extending the applications of WPRF. Specifically, we construct a semi-adaptively secure offline witness encryption (OWE) scheme using a pWPRF, an indistinguishability obfuscation (iO) and a symmetric-key encryption (SKE), which enables us to encrypt messages along with NP statements. We show that replacing iO with extractability obfuscation, the OWE turns out to be an extractable offline witness encryption scheme. To gain finer control over data, we further demonstrate how to convert our OWEs into offline functional witness encryption (OFWE) and extractable OFWE. All of our OWEs and OFWEs produce an optimal size ciphertext, in particular, encryption of a message is as small as the size of the message plus the security parameter multiplied with a constant, which is optimal for any public-key encryption scheme. On the other hand, in any previous OWE, the size of a ciphertext increases polynomially with the size of messages. Finally, we show that the WPRF of Pal et al. (ACISP 2019) can be extended to a pWPRF and an extractable pWPRF

Presence attestation: The missing link in dynamic trust bootstrapping

National Research Foundation (NRF) Singapor

Raziel: Private and Verifiable Smart Contracts on Blockchains

Raziel combines secure multi-party computation and proof-carrying code to provide privacy, correctness and verifiability guarantees for smart contracts on blockchains. Effectively solving DAO and Gyges attacks, this paper describes an implementation and presents examples to demonstrate its practical viability (e.g., private and verifiable crowdfundings and investment funds). Additionally, we show how to use Zero-Knowledge Proofs of Proofs (i.e., Proof-Carrying Code certificates) to prove the validity of smart contracts to third parties before their execution without revealing anything else. Finally, we show how miners could get rewarded for generating pre-processing data for secure multi-party computation.Comment: Support: cothority/ByzCoin/OmniLedge

Virtual HSM: Building a Hardware-backed Dependable Cryptographic Store

Cloud computing is being used by almost everyone, from regular consumer to IT specialists, as it is a way to have high availability, geo-replication, and resource elasticity with pay-as-you-go charging models. Another benefit is the minimal management effort and maintenance expenses for its users. However, security is still pointed out as the main reason hindering the full adoption of cloud services. Consumers lose ownership of their data as soon as it goes to the cloud; therefore, they have to rely on cloud provider’s security assumptions and Service Level Agreements regarding privacy and integrity guarantees for their data. Hardware Security Modules (HSMs) are dedicated cryptographic processors, typically used in secure cloud applications, that are designed specifically for the protection of cryptographic keys in all steps of their life cycles. They are physical devices with tamperproof resistance, but rather expensive. There have been some attempts to virtualize HSMs. Virtual solutions can reduce its costs but without much success as performance is incomparable and security guarantees are hard to achieve in software implementations. In this dissertation, we aim at developing a virtualized HSM supported by modern attestation-based trusted hardware in commodity CPUs to ensure privacy and reliability, which are the main requirements of an HSM. High availability will also be achieved through techniques such as cloud-of-clouds replication on top of those nodes. Therefore virtual HSMs, on the cloud, backed with trusted hardware, seem increasingly promising as security, attestation, and high availability will be guaranteed by our solution, and it would be much cheaper and as reliable as having physical HSMs

Personal Data Management Systems: The security and functionality standpoint

International audienceRiding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. These questions must be answered in order to limit the risk of seeing such solutions adopted only by a handful of users and thus leaving the Personal Cloud paradigm to become no more than one of the latest missed attempts to achieve a better regulation of the management of personal data. To this end, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Finally, we discuss several important research challenges remaining to be addressed to achieve a mature PDMS ecosystem

CODBS: A cascading oblivious search protocol optimized for real-world relational database indexes

Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency. We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-of-the-art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions

TrustZone based attestation in secure runtime verification for embedded systems

Dissertação de mestrado integrado em Engenharia InformáticaARM TrustZone é um “Ambiente de Execução Confiável” disponibilizado em processadores da ARM, que equipam grande parte dos sistemas embebidos. Este mecanismo permite assegurar que componentes críticos de uma aplicação executem num ambiente que garante a confidencialidade dos dados e integridade do código, mesmo que componentes maliciosos estejam instalados no mesmo dispositivo. Neste projecto pretende-se tirar partido do TrustZone no contexto de uma framework segura de monitorização em tempo real de sistemas embebidos. Especificamente, pretende-se recorrer a components como o ARM Trusted Firmware, responsável pelo processo de secure boot em sistemas ARM, para desenvolver um mecanismo de atestação que providencie garantias de computação segura a entidades remotas.ARM TrustZone is a security extension present on ARM processors that enables the development of hardware based Trusted Execution Environments (TEEs). This mechanism allows the critical components of an application to execute in an environment that guarantees data confidentiality and code integrity, even when a malicious agent is installed on the device. This projects aims to harness TrustZone in the context of a secure runtime verification framework for embedded devices. Specifically, it aims to harness existing components, namely ARM Trusted Firmware, responsible for the secure boot process of ARM devices, to implement an attestation mechanism that provides proof of secure computation to remote parties.This work has been partially supported by the Portuguese Foundation for Science and Technology (FCT), project REASSURE (PTDC/EEI-COM/28550/2017), co-financed by the European Regional Development Fund (FEDER), through the North Regional Operational Program (NORTE 2020)