Modern applications employ text files widely for providing data storage in a readable format for applications ranging from database systems to mobile phones. Traditional text processing tools are built around a byte-at-a-time sequential processing model that introduces significant branch and cache miss penalties. Recent work has explored an alternative, transposed representation of text, Parabix (Parallel Bit Streams), to accelerate scanning and parsing using SIMD facilities. This paper advocates and develops Parabix as a general framework and toolkit, describing the software toolchain and run-time support that allows applications to exploit modern SIMD instructions for high performance text processing. The goal is to generalize the techniques to ensure that they apply across a wide variety of applications and architectures. The toolchain enables the application developer to write constructs assuming unbounded character streams and Parabix's code translator generates code based on machine specifics (e.g., SIMD register widths). The general argument in support of Parabix technology is made by a detailed performance and energy study of XML parsing across a range of processor architectures. Parabix exploits intra-core SIMD hardware and demonstrates 2×-7× speedup and 4× improvement in energy efficiency when compared with two widely used conventional software parsers, Expat and Apache-Xerces. SIMD implementations across three generations of x86 processors are studied including the new SandyBridge. The 256-bit AVX technology in Intel SandyBridge is compared with the well established 128-bit SSE technology to analyze the benefits and challenges of 3-operand instruction formats and wider SIMD hardware. Finally, the XML program is partitioned into pipeline stages to demonstrate that thread-level parallelism enables the application to exploit SIMD units scattered across the different cores, achieving improved performance (2× on 4 cores) while maintaining single-threaded energy levels.

To understand how social networks evolve over time, graphs representing the networks need to be published periodically or on-demand. The identity of the participants (nodes) must be anonymized to protect the privacy of the individuals and their relationships (edges) to the other members in the social network. We identify a new form of privacy attack, which we name the degree-trail attack. This attack re-identifies the nodes belonging to a target participant from a sequence of published graphs by comparing the degree of the nodes in the published graphs with the degree evolution of a target. The power of this attack is that the adversary can actively influence the degree of the target individual by interacting with the social network. We show that the adversary can succeed with a high probability even if published graphs are anonymized by strongest known privacy preserving techniques in the literature. Moreover, this success does not depend on the distinctiveness of the target nodes nor require the adversary to behave differently from a normal participant. One of our contributions is a formal method to assess the privacy risk of this type of attacks and empirically study the severity on real social network data.