Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare

Towards Applying Cryptographic Security Models to Real-World Systems

The cryptographic methodology of formal security analysis usually works in three steps: choosing a security model, describing a system and its intended security properties, and creating a formal proof of security. For basic cryptographic primitives and simple protocols this is a well understood process and is performed regularly. For more complex systems, as they are in use in real-world settings it is rarely applied, however. In practice, this often leads to missing or incomplete descriptions of the security properties and requirements of such systems, which in turn can lead to insecure implementations and consequent security breaches. One of the main reasons for the lack of application of formal models in practice is that they are particularly difficult to use and to adapt to new use cases. With this work, we therefore aim to investigate how cryptographic security models can be used to argue about the security of real-world systems. To this end, we perform case studies of three important types of real-world systems: data outsourcing, computer networks and electronic payment. First, we give a unified framework to express and analyze the security of data outsourcing schemes. Within this framework, we define three privacy objectives: \emph{data privacy}, \emph{query privacy}, and \emph{result privacy}. We show that data privacy and query privacy are independent concepts, while result privacy is consequential to them. We then extend our framework to allow the modeling of \emph{integrity} for the specific use case of file systems. To validate our model, we show that existing security notions can be expressed within our framework and we prove the security of CryFS---a cryptographic cloud file system. Second, we introduce a model, based on the Universal Composability (UC) framework, in which computer networks and their security properties can be described We extend it to incorporate time, which cannot be expressed in the basic UC framework, and give formal tools to facilitate its application. For validation, we use this model to argue about the security of architectures of multiple firewalls in the presence of an active adversary. We show that a parallel composition of firewalls exhibits strictly better security properties than other variants. Finally, we introduce a formal model for the security of electronic payment protocols within the UC framework. Using this model, we prove a set of necessary requirements for secure electronic payment. Based on these findings, we discuss the security of current payment protocols and find that most are insecure. We then give a simple payment protocol inspired by chipTAN and photoTAN and prove its security within our model. We conclude that cryptographic security models can indeed be used to describe the security of real-world systems. They are, however, difficult to apply and always need to be adapted to the specific use case

A Hybrid Verifiable and Delegated Cryptographic Model in Cloud Computing

التحكم بالوصول مهم جدا في تبادل البيانات السحابية. و خاصة في مجالات مثل الرعاية الصحية, فمن الضروري ان تكون هناك ألية لمراقبة قائمة الدخول من اجل السرية و الوصول الامن للبيانات. و قد تم التشفير القائم على السمة لسنوات عديدة لتأمين البيانات و توفير الوصول المراقب. في هذا البحث اقترحنا اطاراً يدعم آلية التشفير الدارة و السمة التي تتضمن اطرافا متعددة. هم مالك البيانات , مستخدم البيانات , خادم السحابة و سلطة السمة. ومن السمات الهامة للنظام المقترح هو التفويض الذي يمكن التحقق منه لعملية فك التشفير الى خادم السحابة. مالك البيانات يقوم بتشفير البيانات و مندوبين عملية فك التشفير الى السحابة. خادم السحابة يؤدي فك التشفير الجزئي و من ثم يتم مشاركة بيانات فك التشفير النهائي للمستخدمين وفقاً للامتيازات. مالك البيانات يقلل من التعقيد الحسابي من خلال تفويض خادم السحابة علمية فك التشفير. قمنا ببناء تطبيق النموذج الاولي باستخدام منصة مايكروسوفت دوت نت لأثبات هذا المفهوم. و أظهرت النتائج التجريبية أن هناك وصولا خاضعا للرقابة مع تعدد أدوار المستعملين و حقوق التحكم في النفاذ من أجل النفاذ الآمن و السري إلى البيانات في الحوسبة السحابية.Access control is very important in cloud data sharing. Especially in the domains like healthcare, it is essential to have access control mechanisms in place for confidentiality and secure data access. Attribute based encryption has been around for many years to secure data and provide controlled access. In this paper, we proposed a framework that supports circuit and attributes based encryption mechanism that involves multiple parties. They are data owner, data user, cloud server and attribute authority. An important feature of the proposed system is the verifiable delegation of the decryption process to cloud server. Data owner encrypts data and delegates decryption process to cloud. Cloud server performs partial decryption and then the final decrypted data are shared for users as per the privileges. Data owner  thus reduces computational complexity by delegating decryption process cloud server. We built a prototype application using the Microsoft.NET platform for proof of the concept. The empirical results revealed that there is controlled access with multiple user roles and access control rights for secure and confidential data access in cloud computing

Anonymous RFID authentication for cloud services

Cloud computing is one of the fastest growing segments of IT industry since the users’ commitments for investment and operations are minimized, and costs are in direct relation to usage and demand. In general, cloud services are required to authenticate the user and most of the practical cloud services do not provide anonymity of the users. Namely, cloud provider can track the users easily, so privacy and authenticity are two critical aspects of security. Anonymous authentication is a technique enabling users to prove that they have privilege without disclosing real identities. This type of authentication can be useful especially in scenarios where it is sufficient to ensure the server that the claiming parties are indeed registered. Some motivating applications in the cloud for an anonymous authentication protocol are E-commerce, E-voting, E-library, Ecashand mobile agent applications. Many existing anonymous authentication protocols assume absolute trust to the cloud provider in which all private keys are stored. This trust may result in serious security and privacy issues in case of private key leakage from the cloud provider. In this paper, we propose forward secure anonymous and mutual authentication protocols using RFID technology for cloud services. These protocols avoid the trustworthiness to the cloud provider. Meaning that, even if the private keys are obtained from the corrupted tags or from the server owners of these tags cannot be traced from the past authentication actions. In fact, anonymity of the users will still be ensured even the private keys of tags are compromised

Protecting privacy of users in brain-computer interface applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving(PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations