Privacy-preserving genomic computation through program specialization
In this paper, we present a new approach to performing important classes of genomic computations (e.g., search for homologous genes) that makes a significant step towards privacy protection in this domain. Our approach leverages a key property of the human genome, namely that the vast majority of it is shared across humans (and hence public), and consequently relatively little of it is sensitive. Based on this observation, we propose a privacy-protection framework that partitions a genomic computation, distributing the part on sensitive data to the data provider and the part on the pubic data to the user of the data. Such a partition is achieved through program specialization that enables a biocomputing program to perform a concrete execution on public data and a symbolic execution on sensitive data. As a result, the program is simplified into an efficient query program that takes only sensitive genetic data as inputs. We prove the effectiveness of our techniques on a set of dynamic programming algorithms common in genomic computing. We develop a program transformation tool that automatically instruments a legacy program for specialization operations. We also demonstrate that our techniques can greatly facilitate secure multi-party computations on large biocomputing problems.

title: Solving P2P Traffic Identification Problems Via Optimized Support Vector Machines
abstract: Since the emergence of peer-to-peer (P2P) networking in the last 90s, P2P traffic has become one of the most significant portions of the network traffic. Accurate identification of P2P traffic makes great sense for efficient network management and reasonable utility of network resources. Application level classification of P2P traffic, especially without payload feature detection, is still a challenging problem. This paper proposes a new method for P2P traffic identification and application level classification, which merely uses transport layer information. The method uses support vector machines which have been optimized for performing large learning tasks, rendering that this method become more suitable for large network traffic. The experimental results show that this method achieved high efficiency and is suitable for real-time identification. And carefully tuning the parameters could make the method achieve high accuracy.

title: Panalyst: Privacy-Aware Remote Error Analysis on Commodity Software
abstract: Remote error analysis aims at timely detection and remedy of software vulnerabilities through analyzing run-time errors that occur on the client. This objective can only be achieved by offering users effective protection of their private information and minimizing the performance impact of the analysis on their systems without undermining the amount of information the server can access for understanding errors. To this end, we propose in the paper a new technique for privacy-aware remote analysis, called Panalyst. Panalyst includes a client component and a server component. Once a runtime exception happens to an application, Panalyst client sends the server an initial error report that includes only public information regarding the error, such as the length of the packet that triggers the exception. Using an input built from the report, Panalyst server performs a taint analysis and symbolic execution on the application, and adjusts the input by querying the client about the information upon which the execution of the application depends. The client agrees to answer only when the reply does not give away too much user information. In this way, an input that reproduces the error can be gradually built on the server under the client's consent. Our experimental study of this technique demonstrates that it exposes a very small amount of user information, introduces negligible overheads to the client and enables the server to effectively analyze an error.

title: Learning your identity and disease from research papers: information leaks in genome wide association study
abstract: Genome-wide association studies (GWAS) aim at discovering the association between genetic variations, particularly single-nucleotide polymorphism (SNP), and common diseases, which is well recognized to be one of the most important and active areas in biomedical research. Also renowned is the privacy implication of such studies, which has been brought into the limelight by the recent attack proposed by Homer et al. Homer's attack demonstrates that it is possible to identify a GWAS participant from the allele frequencies of a large number of SNPs. Such a threat, unfortunately, was found in our research to be significantly understated. In this paper, we show that individuals can actually be identified from even a relatively small set of statistics, as those routinely published in GWAS papers. We present two attacks. The first one extends Homer's attack with a much more powerful test statistic, based on the correlations among different SNPs described by coefficient of determination (r2). This attack can determine the presence of an individual from the statistics related to a couple of hundred SNPs. The second attack can lead to complete disclosure of hundreds of participants' SNPs, through analyzing the information derived from published statistics. We also found that those attacks can succeed even when the precisions of the statistics are low and part of data is missing. We evaluated our attacks on the real human genomes and concluded that such threats are completely realistic.

title: Side-Channel Leaks in Web Applications: A Reality Today, a Challenge Tomorrow
abstract: With software-as-a-service becoming mainstream, more and more applications are delivered to the client through the Web. Unlike a desktop application, a web application is split into browser-side and server-side components. A subset of the application’s internal information flows are inevitably exposed on the network. We show that despite encryption, such a side-channel information leak is a realistic and serious threat to user privacy. Specifically, we found that surprisingly detailed sensitive information is being leaked out from a number of high-profile, top-of-the-line web applications in healthcare, taxation, investment and web search: an eavesdropper can infer the illnesses/medications/surgeries of the user, her family income and investment secrets, despite HTTPS protection; a stranger on the street can glean enterprise employees' web search queries, despite WPA/WPA2 Wi-Fi encryption. More importantly, the root causes of the problem are some fundamental characteristics of web applications: stateful communication, low entropy input for better interaction, and significant traffic distinctions. As a result, the scope of the problem seems industry-wide. We further present a concrete analysis to demonstrate the challenges of mitigating such a threat, which points to the necessity of a disciplined engineering practice for side-channel mitigations in future web application developments.

title: Precomputed Radiance Transfer Field for Rendering Interreflections in Dynamic Scenes
abstract: In this paper, we introduce a new representation – radiance transfer fields (RTF) – for rendering interreflections in dynamic scenes under low frequency illumination. The RTF describes the radiance transferred by an individual object to its surrounding space as a function of the incident radiance. An important property of RTF is its independence of the scene configuration, enabling interreflection computation in dynamic scenes. Secondly, RTFs naturally fit in with the rendering framework of precomputed shadow fields, incurring negligible cost to add interreflection effects. In addition, RTFs can be used to compute interreflections for both diffuse and glossy objects. We also show that RTF data can be highly compressed by clustered principal component analysis (CPCA), which not only reduces the memory cost but also accelerates rendering. Finally, we present some experimental results demonstrating our techniques.

title: Immortal DB: transaction time support for SQL server
abstract: Immortal DB builds transaction time database support into the SQL Server engine, not in middleware. Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. The system supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the times of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to all updates of a transaction after commit. All versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing. We demonstrate Immortal DB with a moving objects application that tracks cars in the Seattle area.

title: Context-centric proactive information delivery
abstract: Delivering only those information relevant to the needs of teammates is critical for avoiding information overload. Need-driven proactive information delivery has thus been widely investigated in diverse areas spanning from psychology, AI, to Web-based services. CAST, a team-oriented agent architecture, realized proactive information delivery as a built-in agent behavior, which is derived by the capability of anticipating others' information-needs based on a computational shared mental model. However, the first implementation of CAST is limited in managing the dynamic activation and deactivation of teammates' information-needs. This work addresses this issue by proposing a mechanism for organizing and managing the "context" of information-needs, which facilitates information-needs to be activated and deactivated dynamically. We evaluate the proposed approach by comparing its performance with agent teams using only reactive communications (e.g., ask/reply) and with the original CAST agent teams.

title: Log-based recovery for middleware servers
abstract: We have developed new methods for log-based recovery for middleware servers which involve thread pooling, private in-memory states for clients, shared in-memory state and message interactions among middleware servers. Due to the observed rareness of crashes, relatively small size of shared state and infrequency of shared state read/write accesses, we are able to reduce the overhead of message logging and shared state logging while maintaining recovery independence. Checkpointing has a very small impact on ongoing activities while still reducing recovery time. Our recovery mechanism enables client private states to be recovered in parallel after a crash. On a commercial middleware server platform, we have implemented a recovery infrastructure prototype, which demonstrates the manageability of system complexity and shows promising performance results.

title: Fast Omnidirectional 3D Scene Acquisition with an Array of Stereo Cameras
abstract: We present an omnidirectional 3D acquisition system based on a mobile array of high-resolution consumer digital SLR cameras that automatically capture high dynamic range stereo pairs across a full 360-degree panorama. The stereo pairs are augmented with a time-varying lighting pattern created using standard photographic flashes, lenses, and patterned slides. Spacetime stereo techniques are used to generate 3D range images with corresponding color data from the HDR photographs. The multiple range images are aligned with egomotion estimation and ICP registration techniques, and volumetric merging and color texturing algorithms allow the rapid creation of complete 3D models. The resulting system compares favorably with other state of the art 3D acquisition technologies in the resolution and quality of its output, and can be faster and less expensive than 3D laser scanners for digitizing large 3D scenes such as building interiors.