SIM: Secure Interval Membership Testing and Applications to Secure Comparison

The offline-online model is a leading paradigm for practical secure multi-party computation (MPC) protocol design that has successfully reduced the overhead for several prevalent privacy-preserving computation functionalities common to diverse application domains. However, the prohibitive overheads associated with secure comparison -- one of these vital functionalities -- often bottlenecks current and envisioned MPC solutions. Indeed, an efficient secure comparison solution has the potential for significant real-world impact through its broad applications. This work identifies and presents SIM, a secure protocol for the functionality of interval membership testing. This security functionality, in particular, facilitates secure less-than-zero testing and, in turn, secure comparison. A key technical challenge is to support a fast online protocol for testing in large rings while keeping the precomputation tractable. Motivated by the map-reduce paradigm, this work introduces the innovation of (1) computing a sequence of intermediate functionalities on a partition of the input into input blocks and (2) securely aggregating the output from these intermediate outputs. This innovation allows controlling the size of the precomputation through a granularity parameter representing these input blocks\u27 size -- enabling application-specific automated compiler optimizations. To demonstrate our protocols\u27 efficiency, we implement and test their performance in a high-demand application: privacy-preserving machine learning. The benchmark results show that switching to our protocols yields significant performance improvement, which indicates that using our protocol in a plug-and-play fashion can improve the performance of various security applications. Our new paradigm of protocol design may be of independent interest because of its potential for extensions to other functionalities of practical interest

Reconfigurable Security: Edge Computing-based Framework for IoT

In various scenarios, achieving security between IoT devices is challenging since the devices may have different dedicated communication standards, resource constraints as well as various applications. In this article, we first provide requirements and existing solutions for IoT security. We then introduce a new reconfigurable security framework based on edge computing, which utilizes a near-user edge device, i.e., security agent, to simplify key management and offload the computational costs of security algorithms at IoT devices. This framework is designed to overcome the challenges including high computation costs, low flexibility in key management, and low compatibility in deploying new security algorithms in IoT, especially when adopting advanced cryptographic primitives. We also provide the design principles of the reconfigurable security framework, the exemplary security protocols for anonymous authentication and secure data access control, and the performance analysis in terms of feasibility and usability. The reconfigurable security framework paves a new way to strength IoT security by edge computing.Comment: under submission to possible journal publication

Enabling Interactive Analytics of Secure Data using Cloud Kotta

Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page

PS-TRUST: Provably Secure Solution for Truthful Double Spectrum Auctions

Truthful spectrum auctions have been extensively studied in recent years. Truthfulness makes bidders bid their true valuations, simplifying greatly the analysis of auctions. However, revealing one's true valuation causes severe privacy disclosure to the auctioneer and other bidders. To make things worse, previous work on secure spectrum auctions does not provide adequate security. In this paper, based on TRUST, we propose PS-TRUST, a provably secure solution for truthful double spectrum auctions. Besides maintaining the properties of truthfulness and special spectrum reuse of TRUST, PS-TRUST achieves provable security against semi-honest adversaries in the sense of cryptography. Specifically, PS-TRUST reveals nothing about the bids to anyone in the auction, except the auction result. To the best of our knowledge, PS-TRUST is the first provably secure solution for spectrum auctions. Furthermore, experimental results show that the computation and communication overhead of PS-TRUST is modest, and its practical applications are feasible.Comment: 9 pages, 4 figures, submitted to Infocom 201

Programming support for an integrated multi-party computation and MapReduce infrastructure

We describe and present a prototype of a distributed computational infrastructure and associated high-level programming language that allow multiple parties to leverage their own computational resources capable of supporting MapReduce [1] operations in combination with multi-party computation (MPC). Our architecture allows a programmer to author and compile a protocol using a uniform collection of standard constructs, even when that protocol involves computations that take place locally within each participant’s MapReduce cluster as well as across all the participants using an MPC protocol. The highlevel programming language provided to the user is accompanied by static analysis algorithms that allow the programmer to reason about the efficiency of the protocol before compiling and running it. We present two example applications demonstrating how such an infrastructure can be employed.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798

Game Theory Meets Network Security: A Tutorial at ACM CCS

The increasingly pervasive connectivity of today's information systems brings up new challenges to security. Traditional security has accomplished a long way toward protecting well-defined goals such as confidentiality, integrity, availability, and authenticity. However, with the growing sophistication of the attacks and the complexity of the system, the protection using traditional methods could be cost-prohibitive. A new perspective and a new theoretical foundation are needed to understand security from a strategic and decision-making perspective. Game theory provides a natural framework to capture the adversarial and defensive interactions between an attacker and a defender. It provides a quantitative assessment of security, prediction of security outcomes, and a mechanism design tool that can enable security-by-design and reverse the attacker's advantage. This tutorial provides an overview of diverse methodologies from game theory that includes games of incomplete information, dynamic games, mechanism design theory to offer a modern theoretic underpinning of a science of cybersecurity. The tutorial will also discuss open problems and research challenges that the CCS community can address and contribute with an objective to build a multidisciplinary bridge between cybersecurity, economics, game and decision theory