Secure Outsourced Computation on Encrypted Data

Homomorphic encryption (HE) is a promising cryptographic technique that supports computations on encrypted data without requiring decryption first. This ability allows sensitive data, such as genomic, financial, or location data, to be outsourced for evaluation in a resourceful third-party such as the cloud without compromising data privacy. Basic homomorphic primitives support addition and multiplication on ciphertexts. These primitives can be utilized to represent essential computations, such as logic gates, which subsequently can support more complex functions. We propose the construction of efficient cryptographic protocols as building blocks (e.g., equality, comparison, and counting) that are commonly used in data analytics and machine learning. We explore the use of these building blocks in two privacy-preserving applications. One application leverages our secure prefix matching algorithm, which builds on top of the equality operation, to process geospatial queries on encrypted locations. The other applies our secure comparison protocol to perform conditional branching in private evaluation of decision trees. There are many outsourced computations that require joint evaluation on private data owned by multiple parties. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of the recent advances of genome sequencing technology. Due to the sensitivity of genomic data, this data is encrypted using different keys possessed by different data owners. Computing on ciphertexts encrypted with multiple keys is a non-trivial task. Current solutions often require a joint key setup before any computation such as in threshold HE or incur large ciphertext size (at best, grows linearly in the number of involved keys) such as in multi-key HE. We propose a hybrid approach that combines the advantages of threshold and multi-key HE to support computations on ciphertexts encrypted with different keys while vastly reducing ciphertext size. Moreover, we propose the SparkFHE framework to support large-scale secure data analytics in the Cloud. SparkFHE integrates Apache Spark with Fully HE to support secure distributed data analytics and machine learning and make two novel contributions: (1) enabling Spark to perform efficient computation on large datasets while preserving user privacy, and (2) accelerating intensive homomorphic computation through parallelization of tasks across clusters of computing nodes. To our best knowledge, SparkFHE is the first addressing these two needs simultaneously

Goal Formation through Interaction in the Situation Calculus: A Formal Account Grounded in Behavioral Science

Goal reasoning has been attracting much attention in AI recently. Here, we consider how an agent changes its goals as a result of interaction with humans and peers. In particular, we draw upon a model developed in Behavioral Science, the Elementary Pragmatic Model (EPM). We show how the EPM principles can be incorporated into a sophisticated theory of goal change based on the Situation Calculus. The resulting logical theory supports agents with a wide variety of relational styles, including some that we may consider irrational or creative. This lays the foundations for building autonomous agents that interact with humans in a rich and realistic way, as required by advanced Human-AI collaboration applications

Development of a Low Profile, Endoscopic Implant for Long Term Brain Imaging

The increased public awareness of concussion and traumatic brain injury has motivated continued research into the brain, its functions, and especially its response to injury, with a focus on improving the brain’s repair capabilities. However, due to the critical nature of the tissue, it is currently difficult for researchers to acquire high resolution images below the cortex without sacrificing a lab animal. Sacrificing an animal greatly reduces the amount of data that can be obtained from it, making longitudinal studies unappealing or unfeasible because a large number of animals is needed to obtain useful data over multiple time points. Additionally, inter-animal variance can further obfuscate results. The gradient index (GRIN) lens is a form of micro-endoscope that can penetrate the cortex to obtain high resolution, in vivo images when used with a multiphoton microscope system. The lens is implanted through the skull and into the brain, providing a column of material that refracts and refocuses the laser beam, unlike the natural tissue, which scatters light. This dissertation describes the development of a low profile GRIN lens implant system suitable for longitudinal imaging, as well as the co-development of a restraint system to accommodate the new implant on a microscope stage. The imaging protocol is detailed, and images acquired over three months are shown. The developed device drastically reduced the size of implant both above the skull and within the brain tissue compared to previously reported GRIN lenses, while still obtaining the expected high resolution images. This research also found that labelled axons in transgenic mice appear in unique, recognizable patterns which remain consistent over months of imaging, meaning future studies may use the axons themselves as landmarks. An experimental design for analyzing traumatic brain injury is also developed, which could incorporate a future implant

The Cyber Security Evaluation of a Wireless and Wired Smart Electric Meter

In this thesis, an Experimental cyber security evaluation of Wireless Smart Electric Meter has been performed under cyber security attacks. The security integrity of data collection from EPM 6100 Power Quality Wireless Smart Electric Meter under a wireless cyber-attack was evaluated. After which the security integrity of data collection from the same Wireless Smart Electric Meter was evaluated under a different configuration. In this Thesis, we tested three different smart meters for their connectivity under different cybersecurity attacks. We compared the security integrity of the three different smart meters to measure their response under different cybersecurity attacks

Principles for Environmental Risk Assessment of the Sediment Compartment, Proceedings of the Topical Scientific Workshop Helsinki, 7-8 May 2013

The Topical Scientific Workshop on Risk Assessment for the Sediment Compartment was held from 7 to 8 May 2013 at ECHA, bringing together experts in the field of sediment risk assessment to brainstorm and develop updated scientific principles and guidelines for assessing ecological risks of chemical substances for freshwater and marine sediments. Recent developments for particular substance types (such as metals) and a broad understanding of risk assessment methodologies in other regulated products and from schemes outside the EU were part of the discussion. The primary objective of this workshop was to review the state of the art and to develop updated scientific principles and guidelines for assessing ecological risks of chemical substances for freshwater and marine sediments. The discussion elements included: • Discussing the current state of the science on sediment toxicology. • Reviewing current risk assessment frameworks for sediments relying on the extrapolation from the pelagic community, and developing further recommendations on the applicability of these extrapolation approaches. • Addressing the water-sediment interface and the epi-benthonic community. • Establishing links among available lines of laboratory and field evidence on ecotoxicity, bioavailability, and ecosystem quality/function, and specifically for freshwater systems. • Developing general principles applicable to different regulatory schemes, considering the protection goals set forth by current regulatory processes whilst focusing on the regulation of chemicals under REACH and CLP. The broader context is biocides, plant protection products and pharmaceuticals, and broader legal instruments are also relevant, e.g. the Water Framework Directive, the Industrial Emissions Directive, and equivalent regulatory processes in non-EU jurisdictions. The workshop brought together over 100 experts from around the world to set the scientific principles for assessing risks to the sediment compartment in all regulatory contexts. The two-day workshop included general plenary sessions with case studies and topical breakout group sessions, where the participants discussed specific recommendations on how to use scientific knowledge for regulatory purposes.JRC.H.1-Water Resource

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance