Karyn Benson

Key-Private Proxy Re-encryption

Proxy re-encryption (PRE) allows a proxy to convert a ciphertext encrypted under one key into an encryption of the same message under another key. The main idea is to place as little trust and reveal as little information to the proxy as necessary to allow it to perform its translations. At the very least, the proxy should not be able to learn the keys of the participants or the content of the messages it re-encrypts. However, in all prior PRE schemes, it is easy for the proxy to determine between which participants a re-encryption key can transform ciphertexts. This can be a problem in practice. For example, in a secure distributed file system, content owners may want to use the proxy to help re-encrypt sensitive information without revealing to the proxy the identity of the recipients. In this work, we propose key-private (or anonymous) re-encryption keys as an additional useful property of PRE schemes. We formulate a definition of what it means for a PRE scheme to be secure and key-private. Surprisingly, we show that this property is not captured by prior definitions or achieved by prior schemes, including even the secure obfuscation of PRE by Hohenberger et al. (TCC 2007). Finally, we propose the first key-private PRE construction and prove its CPA-security under a simple extension of Decisional Bilinear Diffie Hellman assumption and its key-privacy under the Decision Linear assumption in the standard model.

Do you know where your cloud files are

Clients of storage-as-a-service systems such as Amazons S3 want to be sure that the files they have entrusted to the cloud are available now and will be available in the future. Using protocols from previous work on proofs of retriev-ability and on provable data possession, clients can verify that their files are available now. But these protocols do not guarantee that the files are replicated onto multiple drives or multiple datacenters. Such tests are crucial if cloud storage is to provide resilience to natural disasters and power outages as well as improving the network latency to different parts of the world. In this paper, we study the problem of verifying that a cloud storage provider replicates the data in diverse geolocations. We provide a theoretical framework for verifying this property. Our model accurately determines which Amazon CloudFront location serves content for Planetlab nodes across the continental US. Our work is complementary to the recent paper of Bowers et al., which uses different techniques to verify that files are replicated across multiple drives in a single datacenter.

Estimating internet address space usage through passive measurements

One challenge in understanding the evolution of Internet infrastructure is the lack of systematic mechanisms for monitoring the extent to which allocated IP addresses are actually used. Address utilization has been monitored via actively scanning the entire IPv4 address space. We evaluate the potential to leverage passive network traffic measurements in addition to or instead of active probing. Passive traffic measurements introduce no network traffic overhead, do not rely on unfiltered responses to probing, and could potentially apply to IPv6 as well. We investigate two challenges in using passive traffic for address utilization inference: the limited visibility of a single observation point; and the presence of spoofed IP addresses in packets that can distort results by implying faked addresses are active. We propose a methodology for removing such spoofed traffic on both darknets and live networks, which yields results comparable to inferences made from active probing. Our preliminary analysis reveals a number of promising findings, including novel insight into the usage of the IPv4 address space that would expand with additional vantage points.

Gaining insight into AS-level outages through analysis of internet background radiation

Internet Background Radiation (IBR) is unsolicited network traffic mostly generated by malicious software, e.g., worms, scans. In previous work, we extracted a signal from IBR traffic arriving at a large (/8) segment of unassigned IPv4 address space to identify large-scale disruptions of connectivity at an Autonomous System (AS) granularity, and used our technique to study episodes of government censorship and natural disasters [1]. Here we explore other IBR-derived metrics that may provide insights into the causes of macroscopic connectivity disruptions. We propose metrics indicating packet loss (e.g., due to link congestion) along a path from a specific AS to our observation point. We use three case studies to illustrate how our metrics can help identify packet loss characteristics of an outage. These metrics could be used in the diagnostic component of a semiautomated system for detecting and characterizing large-scale outages.

Lost in Space: Improving Inference of IPv4 Address Space Utilization

One challenge in understanding the evolution of the Internet infrastructure is the lack of systematic mechanisms for monitoring the extent to which allocated IP addresses are actually used. In this paper, we advance the science of inferring IPv4 address space utilization by proposing a novel taxonomy and analyzing and correlating results obtained through different types of measurements. We have previously studied an approach based on passive measurements that can reveal used portions of the address space unseen by active approaches. In this paper, we study such passive approaches in detail, extending our methodology to new types of vantage points and identifying traffic components that most significantly contribute to discovering used IPv4 network blocks. We then combine the results we obtained through passive measurements together with data from active measurement studies, as well as measurements from Border Gateway Protocol and additional data sets available to researchers. Through the analysis of this large collection of heterogeneous data sets, we substantially improve the state of the art in terms of: 1) understanding the challenges and opportunities in using passive and active techniques to study address utilization and 2) knowledge of the utilization of the IPv4 space.

The k -BDH assumption family: bilinear map cryptography from progressively weaker assumptions

Over the past decade bilinear maps have been used to build a large variety of cryptosystems. In addition to new functionality, we have concurrently seen the emergence of many strong assumptions. In this work, we explore how to build bilinear map cryptosystems under progressively weaker assumptions. We propose k-BDH, a new family of progressively weaker assumptions that generalizes the decisional bilinear Diffie-Hellman (DBDH) assumption. We give evidence in the generic group model that each assumption in our family is strictly weaker than the assumptions before it. DBDH has been used for proving many schemes secure, notably identity-based and functional encryption schemes; we expect that our k-BDH will lead to generalizations of many such schemes. To illustrate the usefulness of our k-BDH family, we construct a family of selectively secure Identity-Based Encryption (IBE) systems based on it. Our system can be viewed as a generalization of the Boneh-Boyen IBE, however, the construction and proof require new ideas to fit the family. Our methods can be extended to produce hierarchical IBEs and CCA security; and give a fully secure variant. In addition, we discuss the opportunities and challenges of building new systems under our weaker assumption family.

Competitive analysis of online traffic grooming in WDM rings

This paper addresses the problem of traffic grooming in wavelength-division multiplexing (WDM) rings where connection requests arrive online. Each request specifies a pair of nodes that wish to communicate and also the desired bandwidth of this connection. If the request is to be satisfied, it must be allocated to one or more wavelengths with sufficient remaining capacity. We consider three distinct profit models specifying the profit associated with satisfying a connection request. We give results on offline and online algorithms for each of the three profit models. We use the paradigm of competitive analysis to theoretically analyze the quality of our online algorithms. Finally, experimental results are given to provide insight into the performance of these algorithms in practice.

Leveraging Internet Background Radiation for Opportunistic Network Analysis

For more than a decade, unsolicited traffic sent to unused regions of the address space has provided valuable insight into malicious Internet activities. In this paper, we explore the utility of this traffic, known as Internet Background Radiation (IBR), for a different purpose: as a data source of Internet-wide measurements. We collect and analyze IBR from two large darknets, carefully deconstructing its various components and characterizing them along dimensions applicable to Internet-wide measurements. Intuitively, IBR can provide insight into network properties when traffic from that network contains relevant information and is of sufficient volume. We turn this intuition into a scientific investigation, examining which networks send IBR, identifying components of IBR that enable opportunistic network inferences, and characterizing the frequency and granularity of traffic sources. We also consider the influences of time of collection and position in the address space on our results. We leverage IBR properties in three case studies to show that IBR can supplement existing techniques by improving coverage and/or diversity of analyzable networks while reducing measurement overhead. Our main contribution is a new framework for understanding the circumstances and properties for which unsolicited traffic is an appropriate data source for inference of macroscopic Internet properties, which can help other researchers assess its utility for a given study.

Using adaptive lossless compression to characterize network traffic

Detecting anomalies in network traffic is a challenging task, not only because of the inherent difficulty of identifying anomalies such as intrusions [1] but also because of the sheer volume of data. In this paper, we attempt to extend existing work in the field of steganalysis to the problem of detecting anomalies in network traffic. By losslessly compressing network traffic using an adaptive compression algorithm, we postulate that it is possible to characterize normal network traffic. Once typical traffic has been defined, it is possible to identify anomalous traffic as the traffic that does not compress well.