Function-Private Subspace-Membership Encryption and Its Applications

Boneh, Raghunathan, and Segev (CRYPTO \u2713) have recently put forward the notion of function privacy and applied it to identity-based encryption, motivated by the need for providing predicate privacy in public-key searchable encryption. Intuitively, their notion asks that decryption keys reveal essentially no information on their corresponding identities, beyond the absolute minimum necessary. While Boneh et al. showed how to construct function-private identity-based encryption (which implies predicate-private encrypted keyword search), searchable encryption typically requires a richer set of predicates. In this paper we significantly extend the function privacy framework. First, we introduce the new notion of subspace-membership encryption, a generalization of inner-product encryption, and formalize a meaningful and realistic notion for capturing its function privacy. Then, we present a generic construction of a function-private subspace-membership encryption scheme based on any inner-product encryption scheme. Finally, we show that function-private subspace-membership encryption can be used to construct function-private identity-based encryption. These are the first generic constructions of function-private encryption schemes based on non-function-private ones, resolving one of the main open problems posed by Boneh, Raghunathan, and Segev

General Impossibility of Group Homomorphic Encryption in the Quantum World

Group homomorphic encryption represents one of the most important building blocks in modern cryptography. It forms the basis of widely-used, more sophisticated primitives, such as CCA2-secure encryption or secure multiparty computation. Unfortunately, recent advances in quantum computation show that many of the existing schemes completely break down once quantum computers reach maturity (mainly due to Shor's algorithm). This leads to the challenge of constructing quantum-resistant group homomorphic cryptosystems. In this work, we prove the general impossibility of (abelian) group homomorphic encryption in the presence of quantum adversaries, when assuming the IND-CPA security notion as the minimal security requirement. To this end, we prove a new result on the probability of sampling generating sets of finite (sub-)groups if sampling is done with respect to an arbitrary, unknown distribution. Finally, we provide a sufficient condition on homomorphic encryption schemes for our quantum attack to work and discuss its satisfiability in non-group homomorphic cases. The impact of our results on recent fully homomorphic encryption schemes poses itself as an open question.Comment: 20 pages, 2 figures, conferenc

A New PVSS Scheme with a Simple Encryption Function

A Publicly Verifiable Secret Sharing (PVSS) scheme allows anyone to verify the validity of the shares computed and distributed by a dealer. The idea of PVSS was introduced by Stadler in [18] where he presented a PVSS scheme based on Discrete Logarithm. Later, several PVSS schemes were proposed. In [2], Behnad and Eghlidos present an interesting PVSS scheme with explicit membership and disputation processes. In this paper, we present a new PVSS having the advantage of being simpler while offering the same features.Comment: In Proceedings SCSS 2012, arXiv:1307.8029. This PVSS scheme was proposed to be used to provide a distributed Timestamping schem

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

Flexible Yet Secure De-Duplication Service for Enterprise Data on Cloud Storage

The cloud storage services bring forth infinite storage capacity and flexible access capability to store and share large-scale content. The convenience brought forth has attracted both individual and enterprise users to outsource data service to a cloud provider. As the survey shows 56% of the usages of cloud storage applications are for data back up and up to 68% of data backup are user assets. Enterprise tenants would need to protect their data privacy before uploading them to the cloud and expect a reasonable performance while they try to reduce the operation cost in terms of cloud storage, capacity and I/Os matter as well as systems’ performance, bandwidth and data protection. Thus, enterprise tenants demand secure and economic data storage yet flexible access on their cloud data. In this paper, we propose a secure de-duplication solution for enterprise tenants to leverage the benefits of cloud storage while reducing operation cost and protecting privacy. First, the solution uses a proxy to do flexible group access control which supports secure de-duplication within a group; Second, the solution supports scalable clustering of proxies to support large-scale data access; Third, the solution can be integrated with cloud storage seamlessly. We implemented and tested our solution by integrating it with Dropbox. Secure de-duplication in a group is performed at low data transfer latency and small storage overhead as compared to de-duplication on plaintext

Secure and Trustable Electronic Medical Records Sharing using Blockchain

Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall costComment: AMIA 2017 Annual Symposium Proceeding