Basics of qualitative research: Grounded theory procedures and techniques

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Preliminary Definitions, Results and Motivation Stable Matching Problems: The Stable Marriage Problem: An Update SM and HR with Indifference The Stable Roommates Problem Further Stable Matching Problems Other Optimal Matching Problems: Pareto Optimal Matchings Popular Matchings Profile-Based Optimal Matchings.

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Algorithmics of Matching Under Preferences

Machine learning is widely used to produce models for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and prediction stages. In this paper, we design and implement a general framework for privacy-preserving machine learning and use it to obtain new solutions for training linear regression, logistic regression and neural network models. Our protocols are in a three-server model wherein data owners secret share their data among three servers who train and evaluate models on the joint data using three-party computation (3PC). Our main contribution is a new and complete framework ($\textABY ^3$) for efficiently switching back and forth between arithmetic, binary, and Yao 3PC which is of independent interest. Many of the conversions are based on new techniques that are designed and optimized for the first time in this paper. We also propose new techniques for fixed-point multiplication of shared decimal values that extends beyond the three-party case, and customized protocols for evaluating piecewise polynomial functions. We design variants of each building block that is secure against \em malicious adversaries who deviate arbitrarily. We implement our system in C++. Our protocols are up to \em four orders of magnitude faster than the best prior work, hence significantly reducing the gap between privacy-preserving and plaintext training.

ABY3: A Mixed Protocol Framework for Machine Learning

We give a provable-security treatment for the key-wrap problem, providing definitions, constructions, and proofs. We suggest that key-wrap's goal is security in the sense of deterministic authenticated-encryption (DAE), a notion that we put forward. We also provide an alternative notion, a pseudorandom injection (PRI), which we prove to be equivalent. We provide a DAE construction, SIV, analyze its concrete security, develop a blockcipher-based instantiation of it, and suggest that the method makes a desirable alternative to the schemes of the X9.102 draft standard. The construction incorporates a method to turn a PRF that operates on a string into an equally efficient PRF that operates on a vector of strings, a problem of independent interest. Finally, we consider IV-based authenticated-encryption (AE) schemes that are maximally forgiving of repeated IVs, a goal we formalize as misuse-resistant AE. We show that a DAE scheme with a vector-valued header, such as SIV, directly realizes this goal.

A provable-security treatment of the key-wrap problem

In this paper we define the problem and scope of data sovereignty - the coupling of stored data authenticity and geographical location in the cloud. Establishing sovereignty is an especially important concern amid legal and policy constraints when data and resources are virtualized and widely distributed. We identify the key challenges that need to be solved to achieve an effective and un-cheatable solution as well as propose an initial technique for data sovereignty.

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A position paper on data sovereignty: the importance of geolocating data in the cloud

We introduce and analyze a general framework for authentically binding data to a location while providing strong assurances against cloud storage providers that (either accidentally or maliciously) attempt to re-locate cloud data. We then evaluate a preliminary solution in this framework that combines constraint-based host geolocation with proofs of data possession, called constraint-based data geolocation (CBDG). We evaluate CBDG using a combination of experiments with PlanetLab and real cloud storage services, demonstrating that we can bind fetched data to the location originally hosting it with high precision. We geolocate data hosted on the majority of our PlanetLab targets to regions no larger than 118,000 km^2, and we geolocate data hosted on Amazon S3 to an area no larger than 12,000 km^2, sufficiently small to identify the state or service region.

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Geolocation of data in the cloud

The US Naval Postgraduate School and University of Washington each independently developed informal security-themed tabletop games. [d0x3d!] is a board game in which players collaborate as white-hat hackers, tasked to retrieve a set of valuable digital assets held by an adversarial network. Control-Alt-Hack is a card game in which three to six players act as white-hat hackers at a security consulting company. These games employ modest pedagogical objectives to expose broad audiences to computer security topics.

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Security through play

While solutions for file system encryption can pre- vent an adversary from determining the contents of files, in situations where a user wishes to hide the existence of data, encryption alone is not sufficient. Indeed, encryption may draw attention to those files, as they may likely contain information the user wishes to keep secret. Consequently, adversarial coercion may motivate the owner to surrender their encryption keys, under duress. This paper presents DEFY, a deniable file system following a log-structured design. Maintaining a log-structure is motivated by the technical constraints imposed by solid-state drives, such as those found in mobile devices. These devices have consequential properties that previous work largely ignores. Further, DEFY provides features not offered by prior work, including: authenticated encryption, fast secure deletion, and support for multiple layers of deniability. We consider security against a snapshot adversary, the strongest deniable filesystem adversary considered by prior literature. We have implemented a prototype based on YAFFS and an evaluation shows DEFY exhibits performance degradation comparable to the encrypted file system for flash, WhisperYAFFS.

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DEFY: A Deniable, Encrypted File System for Log-Structured Storage

Without a threat model and a system model, voting standards cannot ensure the integrity or accuracy of the voting process.

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Mark Gondree

Papers

A position paper on data sovereignty: the importance of geolocating data in the cloud

Geolocation of data in the cloud

Security through play

DEFY: A Deniable, Encrypted File System for Log-Structured Storage

Fixing federal e-voting standards