Holographic Storage is an old idea that has always promised high density and fast random access, but has never been commercially competitive with Hard Disk Drives (HDDs) and Solid State Devices (SSDs). In Project HSD at Microsoft Research we asked the question: “Does holographic storage finally make sense for cloud storage?” This article describes our journey toward answering this question. We achieved 1.8× higher density than the previous state-of-the-art, using commodity components available today and leveraging machine learning to compensate for the noise and distortions introduced by commodity components. This uncovered two new challenges which are the focus of this article: achieving high end-to-end energy efficiency without sacrificing capacity, and spatial multiplexing without mechanical movement. Improving end-to-end energy efficiency requires joint optimization across low-level media parameters and higher-level system parameters that govern background maintenance operations such as read refresh and garbage collection. We developed new physics models of the media; analytic and simulation models of the media access and background media maintenance; and workload-driven optimization to find optimal parameter combinations. These techniques resulted in a 14× improvement over the previous approach for typical workloads without sacrificing capacity. We also designed the first scalable and mechanical movement free spatial multiplexing system for holographic storage. Despite these advances, we conclude that currently, holographic storage is still far from the combination of density, capacity scaling, and energy efficiency needed to compete with the incumbent technologies. We need fundamental advances in the physical media that improve energy efficiency by another 1–2 orders of magnitude without reducing data density. Further advances in optics are also required to achieve spatial multiplexing that is simultaneously scalable, low-loss, and high-density.

In the boundless digital world and global society of the Internet, anonymity and privacy are becoming increasingly important issues. Many anonymous networking protocols have been proposed and numerous empirical investigations over these networks analyzed; however, no known taxonomies exist for consumers to quickly and easily identify which anonymity properties are offered by the disparate wired and wireless anonymous networks. This paper proposes a novel tree-based taxonomy (TBT) which allows the consumer to choose a desired classical or state-of-the-art anonymity protocol depending upon their specific network environment and anonymity requirements. The two key anonymity properties of unidentifiability and unlinkability and various network types are explored. This paper highlights the expanding definition of anonymity and aids consumer and researcher classification of state-of-the-art technological privacy-preserving anonymous networks.

With the aim to preserve privacy over a communications network, a plethora of anonymous protocols have been proposed along with many empirical investigations into specific adversary attacks over those networks. However, no known taxonomies exist that address anonymity in the diverse set of both wired and wireless anonymous communications networks. This paper proposes such a novel cubic taxonomy which explores the three key components of anonymity property, adversary capability, and network type. This taxonomy expands the definition of anonymity and aids in the advancement of state-of-the-art technological privacy-preserving mechanisms in anonymous networks against any adversary.

With the aim to preserve privacy over a communications network, a plethora of anonymous protocols have been proposed along with many empirical investigations into specific adversary attacks over those networks. However, few formal methods have been adequately developed and applied towards anonymous systems with the goal of modeling how an adversary reasons about anonymity. Indeed, many analyses assume a passive, global adversary but fail to provide a rigorous approach to defining and modeling anonymity concepts to ensure information and data assurance as is customary when formally proving other security aspects of a system. Hence, this paper proposes the possibilistic anonymity logical model (PALM) for capturing the knowledge and reasoning ability of an adversary in an anonymous network.

A novel client puzzle protocol, the memoryless puzzle protocol (MPP), is proposed and investigated. The goal is to show that MPP is a viable solution for mitigating distributed denial-of-service (DDoS) attacks in an anonymous routing environment. One such environment, Tor, provides anonymity for interactive Internet services. However, Tor relies on the transport layer security (TLS) protocol, making it vulnerable to distributed denial-of-service (DDoS) attacks. Although client puzzles are often proposed as a solution to denial-of-service attacks, this research is the first to explore TLS DDoS attack mitigation in the Tor anonymous routing environment. Using the MPP, the central processing unit (CPU) utilization and user-data latency measures are analyzed under four increasing DDoS attack intensities and four different puzzle probability distribution levels. For results, typical CPU utilization rates of 80-100% drop to below 70% signifying successful mitigation. Furthermore, even if a client only has a 30% chance of receiving a puzzle or the maximum puzzle strength is used, MPP effectively mitigates attacks. Finally, user-data latency decreases approximately 50% under large-scale attacks. Hence, the MPP is a suitable solution for increasing the robustness and reliability of Tor.