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

Entangled cloud storage

Entangled cloud storage (Aspnes et al., ESORICS 2004) enables a set of clients to “entangle” their files into a single clew to be stored by a (potentially malicious) cloud provider. The entanglement makes it impossible to modify or delete significant part of the clew without affecting all files encoded in the clew. A clew keeps the files in it private but still lets each client recover his own data by interacting with the cloud provider; no cooperation from other clients is needed. At the same time, the cloud provider is discouraged from altering or overwriting any significant part of the clew as this will imply that none of the clients can recover their files. We put forward the first simulation-based security definition for entangled cloud storage, in the framework of universal composability (Canetti, 2001). We then construct a protocol satisfying our security definition, relying on an entangled encoding scheme based on privacy-preserving polynomial interpolation; entangled encodings were originally proposed by Aspnes et al. as useful tools for the purpose of data entanglement. As a contribution of independent interest we revisit the security notions for entangled encodings, putting forward stronger definitions than previous work (that for instance did not consider collusion between clients and the cloud provider). Protocols for entangled cloud storage find application in the cloud setting, where clients store their files on a remote server and need to be ensured that the cloud provider will not modify or delete their data illegitimately. Current solutions, e.g., based on Provable Data Possession and Proof of Retrievability, require the server to be challenged regularly to provide evidence that the clients’ files are stored at a given time. Entangled cloud storage provides an alternative approach where any single client operates implicitly on behalf of all others, i.e., as long as one client's files are intact, the entire remote database continues to be safe and unblemishe

Secret Sharing and Network Coding

In this thesis, we consider secret sharing schemes and network coding. Both of these fields are vital in today\u27s age as secret sharing schemes are currently being implemented by government agencies and private companies, and as network coding is continuously being used for IP networks. We begin with a brief overview of linear codes. Next, we examine van Dijk\u27s approach to realize an access structure using a linear secret sharing scheme; then we focus on a much simpler approach by Tang, Gao, and Chen. We show how this method can be used to find an optimal linear secret sharing scheme for an access structure with six participants. In the last chapter, we examine network coding and point out some similarities between secret sharing schemes and network coding. We present results from a paper by Silva and Kschischang; in particular, we present the concept of universal security and their coset coding scheme to achieve universal security

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

Functional Encryption as Mediated Obfuscation

We introduce a new model for program obfuscation, called mediated obfuscation. A mediated obfuscation is a 3-party protocol for evaluating an obfuscated program that requires minimal interaction and limited trust. The party who originally supplies the obfuscated program need not be online when the client wants to evaluate the program. A semi-trusted third-party mediator allows the client to evaluate the program, while learning nothing about the obfuscated program or the client’s inputs and outputs. Mediated obfuscation would provide the ability for a software vendor to safely outsource the less savory aspects (like accounting of usage statistics, and remaining online to facilitate access) of “renting out” access to proprietary software. We give security definitions for this new obfuscation paradigm, and then present a simple and generic construction based on functional encryption. If a functional encryption scheme supports decryption functionality F (m, k), then our construction yields a mediated obfuscation of the class of functions {F (m, ·) | m}. In our construction, the interaction between the client and the mediator is minimal (much more efficient than a general- purpose multi-party computation protocol). Instantiating with existing FE constructions, we achieve obfuscation for point-functions with output (under a strong “virtual black-box” notion of security), and a general feasibility result for obfuscating conjunctive normal form and disjunctive normal form formulae (under a weaker “semantic” notion of security). Finally, we use mediated obfuscation to illustrate a connection between worst-case and average-case static obfuscation. In short, an average-case (static) obfuscation of some component of a suitable functional encryption scheme yields a worst-case (static) obfuscation for a related class of functions. We use this connection to demonstrate new impossibility results for average-case (static) obfuscation

The Crypto-democracy and the Trustworthy

In the current architecture of the Internet, there is a strong asymmetry in terms of power between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud providers, ...) and the individuals from which this personal data is issued. In particular, individuals have no choice but to blindly trust that these entities will respect their privacy and protect their personal data. In this position paper, we address this issue by proposing an utopian crypto-democracy model based on existing scientific achievements from the field of cryptography. More precisely, our main objective is to show that cryptographic primitives, including in particular secure multiparty computation, offer a practical solution to protect privacy while minimizing the trust assumptions. In the crypto-democracy envisioned, individuals do not have to trust a single physical entity with their personal data but rather their data is distributed among several institutions. Together these institutions form a virtual entity called the Trustworthy that is responsible for the storage of this data but which can also compute on it (provided first that all the institutions agree on this). Finally, we also propose a realistic proof-of-concept of the Trustworthy, in which the roles of institutions are played by universities. This proof-of-concept would have an important impact in demonstrating the possibilities offered by the crypto-democracy paradigm.Comment: DPM 201

Input-shrinking functions: theory and application

In this thesis, we contribute to the emerging field of the Leakage-Resilient Cryptography by studying the problem of secure data storage on hardware that may leak information, introducing a new primitive, a leakage-resilient storage, and showing two different constructions of such storage scheme provably secure against a class of leakage functions that can depend only on some restricted part of the memory and against a class of computationally weak leakage functions, e.g. functions computable by small circuits, respectively. Our results come with instantiations and analysis of concrete parameters. Furthermore, as second contribution, we present our implementation in C programming language, using the cryptographic library of the OpenSSL project, of a two-party Authenticated Key Exchange (AKE) protocol, which allows a client and a server, who share a huge secret file, to securely compute a shared key, providing client-to-server authentication, also in the presence of active attackers. Following the work of Cash et al. (TCC 2007), we based our construction on a Weak Key Exchange (WKE) protocol, developed in the BRM, and a Password-based Authenticated Key Exchange (PAKE) protocol secure in the Universally Composable (UC) framework. The WKE protocol showed by Cash et al. uses an explicit construction of averaging sampler, which uses less random bits than the random choice but does not seem to be efficiently implementable in practice. In this thesis, we propose a WKE protocol similar but simpler than that one of Cash et al.: our protocol uses more randomness than the Cash et al.'s one, as it simply uses random choice instead of averaging sampler, but we are able to show an efficient implementation of it. Moreover, we formally adapt the security analysis of the WKE protocol of Cash et al. to our WKE protocol. To complete our AKE protocol, we implement the PAKE protocol showed secure in the UC framework by Abdalla et al. (CT-RSA 2008), which is more efficient than the Canetti et al.'s UC-PAKE protocol (EuroCrypt 2005) used in Cash et al.'s work. In our implementation of the WKE protocol, to achieve small constant communication complexity and amount of randomness, we rely on the Random Oracle (RO) model. However, we would like to note that in our implementation of the AKE protocol we need also a UC-PAKE protocol which already relies on RO, as it is impossible to achieve UC-PAKE in the standard model. In our work we focus not only on the theoretical aspects of the area, providing formal models and proofs, but also on the practical ones, analyzing instantiations, concrete parameters and implementation of the proposed solutions, to contribute to bridge the gap between theory and practice in this field

Secure Computing, Economy, and Trust: A Generic Solution for Secure Auctions with Real-World Applications

In this paper we consider the problem of constructing secure auctions based on techniques from modern cryptography. We combine knowledge from economics, cryptography and security engineering and develop and implement secure auctions for practical real-world problems. In essence this paper is an overview of the research project SCET--Secure Computing, Economy, and Trust-- which attempts to build auctions for real applications using secure multiparty computation. The main contributions of this project are: A generic setup for secure evaluation of integer arithmetic including comparisons; general double auctions expressed by such operations; a real world double auction tailored to the complexity and performance of the basic primitives '+' and