Memoization Attacks and Copy Protection in Partitioned Applications

Application source code protection is a major concern for software architects today. Secure platforms have been proposed that protect the secrecy of application algorithms and enforce copy protection assurances. Unfortunately, these capabilities incur a sizeable performance overhead. Partitioning an application into secure and insecure regions can help diminish these overheads but invalidates guarantees of code secrecy and copy protection.This work examines one of the problems of partitioning an application into public and private regions, the ability of an adversary to recreate those private regions. To our knowledge, it is the first to analyze this problem when considering application operation as a whole. Looking at the fundamentals of the issue, we analyze one of the simplest attacks possible, a ``Memoization Attack.'' We implement an efficient Memoization Attack and discuss necessary techniques that limit storage and computation consumption. Experimentation reveals that certain classes of real-world applications are vulnerable to Memoization Attacks. To protect against such an attack, we propose a set of indicator tests that enable an application designer to identify susceptible application code regions

Learning biophysically-motivated parameters for alpha helix prediction

Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological “pseudo-energies. ” Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Results: Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Qα value of 77.6 % and an SOVα value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. Conclusions: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here. 1 Backgroun

Secure application partitioning for intellectual property protection

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 79-83).Intellectual property protection is a major concern for both hardware and software architects today. Recently secure platforms have been proposed to protect the privacy of application code and enforce that an application can only be run or accessed by authorized hosts. Unfortunately, these capabilities incur a sizeable performance overhead. Partitioning an application into secure and insecure regions can help diminish overheads but invalidates guarantees of privacy and access control. This work examines the problem of securely partitioning an application into public and private regions so that private code confidentiality is guaranteed and only authorized hosts can execute the application. This problem must be framed within the context of whole application execution for any solution to have meaning, which is a critical point when evaluating software security. The adversarial model presented balances practical generality with concrete security guarantees, and it is shown that under this model the best attack possible is a Memoization Attack." A practical Memoization Attack is implemented, and experimentation reveals that naive partitioning strategies can expose the functionality of hidden code in real applications, allowing unauthorized execution. To protect against such an attack, a set of indicators are presented that enable an application designer to identify these insecure application code regions. Finally, a partitioning methodology is discussed that uses these indicators to partition an application in a manner that protects the privacy of intellectual property and prohibits unauthorized execution.by Charles W. O'Donnell.S.M

STITCHER: Dynamic assembly of likely amyloid and prion β-structures from secondary structure predictions

The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Previous work has demonstrated that probability-based prediction of discrete β-strand pairs can offer insight into these structures. Here, we devise a system of energetic rules that can be used to dynamically assemble these discrete β-strand pairs into complete amyloid β-structures. The STITCHER algorithm progressively ‘stitches’ strand-pairs into full β-sheets based on a novel free-energy model, incorporating experimentally observed amino-acid side-chain stacking contributions, entropic estimates, and steric restrictions for amyloidal parallel β-sheet construction. A dynamic program computes the top 50 structures and returns both the highest scoring structure and a consensus structure taken by polling this list for common discrete elements. Putative structural heterogeneity can be inferred from sequence regions that compose poorly. Predictions show agreement with experimental models of Alzheimer's amyloid beta peptide and the Podospora anserina Het-s prion. Predictions of the HET-s homolog HET-S also reflect experimental observations of poor amyloid formation. We put forward predicted structures for the yeast prion Sup35, suggesting N-terminal structural stability enabled by tyrosine ladders, and C-terminal heterogeneity. Predictions for the Rnq1 prion and alpha-synuclein are also given, identifying a similar mix of homogenous and heterogeneous secondary structure elements. STITCHER provides novel insight into the energetic basis of amyloid structure, provides accurate structure predictions, and can help guide future experimental studies. Proteins 2011

Discovery of non-directional and directional pioneer transcription factors by modeling DNase profile magnitude and shape

Here we describe Protein Interaction Quantitation (PIQ), a computational method that models the magnitude and shape of genome-wide DNase profiles to facilitate the identification of transcription factor (TF) binding sites. Through the use of machine learning techniques, PIQ identified binding sites for >700 TFs from one DNase-seq experiment with accuracy comparable to ChIP-seq for motif-associated TFs (median AUC=0.93 across 303 TFs). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into pre-pancreatic and intestinal endoderm. We identified (n=120) and experimentally validated eight ‘pioneer’ TF families that dynamically open chromatin, enabling other TFs to bind to adjacent DNA. Four pioneer TF families only open chromatin in one direction from their motifs. Furthermore, we identified a class of ‘settler’ TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and non-directional pioneer activity shapes the chromatin landscape for population by settler TFs

Pion-nucleus elastic scattering on 12C, 40Ca, 90Zr, and 208Pb at 400 and 500 MeV

Pion-nucleus elastic scattering at energies above the Delta(1232) resonance is studied using both pi+ and pi- beams on 12C, 40Ca, 90Zr, and 208Pb. The present data provide an opportunity to study the interaction of pions with nuclei at energies where second-order corrections to impulse approximation calculations should be small. The results are compared with other data sets at similar energies, and with four different first-order impulse approximation calculations. Significant disagreement exists between the calculations and the data from this experiment

HDAC9 is implicated in atherosclerotic aortic calcification and affects vascular smooth muscle cell phenotype.

Aortic calcification is an important independent predictor of future cardiovascular events. We performed a genome-wide association meta-analysis to determine SNPs associated with the extent of abdominal aortic calcification (n = 9,417) or descending thoracic aortic calcification (n = 8,422). Two genetic loci, HDAC9 and RAP1GAP, were associated with abdominal aortic calcification at a genome-wide level (P < 5.0 × 10-8). No SNPs were associated with thoracic aortic calcification at the genome-wide threshold. Increased expression of HDAC9 in human aortic smooth muscle cells promoted calcification and reduced contractility, while inhibition of HDAC9 in human aortic smooth muscle cells inhibited calcification and enhanced cell contractility. In matrix Gla protein-deficient mice, a model of human vascular calcification, mice lacking HDAC9 had a 40% reduction in aortic calcification and improved survival. This translational genomic study identifies the first genetic risk locus associated with calcification of the abdominal aorta and describes a previously unknown role for HDAC9 in the development of vascular calcification

Feasibility, Safety, Acceptability, and Preliminary Efficacy of Measurement-Based Care Depression Treatment for HIV Patients in Bamenda, Cameroon

Depression affects 18-30% of HIV-infected patients in Africa and is associated with greater stigma, lower antiretroviral adherence, and faster disease progression. However, the region's health system capacity to effectively identify and treat depression is limited. Task-shifting models may help address this large mental health treatment gap

MARIS: Method for Analyzing RNA following Intracellular Sorting

Transcriptional profiling is a key technique in the study of cell biology that is limited by the availability of reagents to uniquely identify specific cell types and isolate high quality RNA from them. We report a Method for Analyzing RNA following Intracellular Sorting (MARIS) that generates high quality RNA for transcriptome profiling following cellular fixation, intracellular immunofluorescent staining and FACS. MARIS can therefore be used to isolate high quality RNA from many otherwise inaccessible cell types simply based on immunofluorescent tagging of unique intracellular proteins. As proof of principle, we isolate RNA from sorted human embryonic stem cell-derived insulin-expressing cells as well as adult human β cells. MARIS is a basic molecular biology technique that could be used across several biological disciplines.Howard Hughes Medical InstituteHarvard Stem Cell InstituteNational Institutes of Health (U.S.) (grant 2U01DK07247307)National Institutes of Health (U.S.) (grant RL1DK081184)National Institutes of Health (U.S.) (grant 1U01HL10040804