Privacy-Enhancing Methods for Time Series and their Impact on Electronic Markets

The amount of collected time series data containing personal information has increased in the last years, e.g., smart meters store time series of power consumption data. Using such data for the benefit of society requires methods to protect the privacy of individuals. Those methods need to modify the data. In this thesis, we contribute a provable privacy method for time series and introduce an application specific measure in the smart grid domain to evaluate their impact on data quality

Learning From (Failed) Replications: Cognitive Load Manipulations and Charitable Giving

Replication of empirical studies is much more than a tool to police the field. Failed replications force us to recognize that seemingly arbitrary design features may impact results in important ways. We describe a study that used a cognitive load manipulation to investigate the role of the deliberative system in charitable giving and a set of failed replications of that study. While the original study showed large and statistically significant results, we failed to replicate using the same protocol and the same subject pool. After the first failed replication, we hypothesized that the order our study was taken in a set of unrelated studies in a laboratory session generated the differences in effects. Three more replication attempts supported this hypothesis. The study demonstrates the importance of replication in advancing our understanding of the mechanisms driving a particular result and it questions the robustness of results established by cognitive load tests

Deploying and Evaluating Pufferfish Privacy for Smart Meter Data (Technical Report)

Information hiding ensures privacy by transforming personalized data so that certain sensitive information cannot be inferred any more. One state-of-the-art information-hiding approach is the Pufferfish framework. It lets the users specify their privacy requirements as so-called discriminative pairs of secrets, and it perturbs data so that an adversary does not learn about the probability distribution of such pairs. However, deploying the framework on complex data such as time series requires application specific work. This includes a general definition of the representation of secrets in the data. Another issue is that the tradeoff between Pufferfish privacy and utility of the data is largely unexplored in quantitative terms. In this study, we quantify this tradeoff for smart meter data. Such data contains fine-grained time series of power-consumption data from private households. Disseminating such data in an uncontrolled way puts privacy at risk. We investigate how time series of energy consumption data must be transformed to facilitate specifying secrets that Pufferfish can use. We ensure the generality of our study by looking at different information-extraction approaches, such as re-identification and non-intrusive-appliance-load monitoring, in combination with a comprehensive set of secrets. Additionally, we provide quantitative utility results for a real-world application, the so-called local energy market

Pattern-sensitive Time-series Anonymization and its Application to Energy-Consumption Data

Time series anonymization is an important problem. One prominent example of time series are energy consumption records, which might reveal details of the daily routine of a household. Existing privacy approaches for time series, e.g., from the field of trajectory anonymization, assume that every single value of a time series contains sensitive information and reduce the data quality very much. In contrast, we consider time series where it is combinations of tuples that represent personal information. We propose (n; l; k)-anonymity, geared to anonymization of time-series data with minimal information loss, assuming that an adversary may learn a few data points. We propose several heuristics to obtain (n; l; k)-anonymity, and we evaluate our approach both with synthetic and real data. Our experiments confirm that it is sufficient to modify time series only moderately in order to fulfill meaningful privacy requirements

Odin (ANKS1A) is a Src family kinase target in colorectal cancer cells

<p>Abstract</p> <p>Background</p> <p>Src family kinases (SFK) are implicated in the development of some colorectal cancers (CRC). One SFK member, Lck, is not detectable in normal colonic epithelium, but becomes aberrantly expressed in a subset of CRCs. Although SFK have been extensively studied in fibroblasts and different types of immune cells, their physical and functional targets in many epithelial cancers remain poorly characterised.</p> <p>Results</p> <p>64 CRC cell lines were tested for expression of Lck. SW620 CRC cells, which express high levels of Lck and also contain high basal levels of tyrosine phosphorylated (pY) proteins, were then analysed to identify novel SFK targets. Since SH2 domains of SFK are known to often bind substrates after phosphorylation by the kinase domain, the LckSH2 was compared with 14 other SH2s for suitability as affinity chromatography reagent. Mass spectrometric analyses of LckSH2-purified pY proteins subsequently identified several proteins readily known as SFK kinase substrates, including cortactin, Tom1L1 (SRCASM), GIT1, vimentin and AFAP1L2 (XB130). Additional proteins previously reported as substrates of other tyrosine kinase were also detected, including the EGF and PDGF receptor target Odin. Odin was further analysed and found to contain substantially less pY upon inhibition of SFK activity in SW620 cells, indicating that it is a formerly unknown SFK target in CRC cells.</p> <p>Conclusion</p> <p>Rapid identification of known and novel SFK targets in CRC cells is feasible with SH2 domain affinity chromatography. The elucidation of new SFK targets like Odin in epithelial cancer cells is expected to lead to novel insight into cancer cell signalling mechanisms and may also serve to indicate new biomarkers for monitoring tumor cell responses to drug treatments.</p

Development of guidance for non-market approaches in the Paris Agreement: operationalizing Articles 6.8 and 6.9 of the Paris Agreement

While market-based forms of cooperation are enshrined in Articles 6.2–6.7, Article 6.8 of the Paris Agreement recognizes the importance of non-market approaches (NMAs) in international cooperation on climate change mitigation and adaptation in a variety of fields. Article 6.9 establishes the NMA framework that promotes NMAs described in Article 6.8. The Parties to the Paris Agreement are currently negotiating a work program to further elaborate on this. If properly designed, fostering the accelerated diffusion of non-market based international cooperation on technology development and transfer, capacity-building and finance in both adaptation and mitigation can provide a relevant contribution to NDC implementation and ratcheting up of ambition. Having that goal in mind, this report provides recommendations on the operationalization of the NMA framework and the work program and the identification of concrete NMAs for consideration by the negotiating Parties. We provide concrete examples of NMAs in various fields Parties have identified as relevant under the framework, including forests, resilience, removals, energy efficiency and the cross cutting topics mentioned above. The NMA work program should be designed as a meaningful addition to ongoing work under the United Nations Framework Convention on Climate Change. The focus must be on activities that are not duplicating ongoing efforts, not implementable through markets, transformative, and have so far been side-lined by international public climate finance. The NMAs’ relevance will ultimately depend on Parties’ active engagement in the identification of concrete NMAs and their submission to the NMA forum envisaged in the latest iterations of the Presidency draft texts from COP25. The NMA forum should operate in a flexible but results-oriented manner to allow for the consideration of emerging concepts and pilot activities. In the end, the role of finance will also be pivotal for the work program’s relevance. According to the current status of negotiations, the work program will not have own financial resources but the consideration of finance is essential to avoid that the NMA work program becomes a mere ‘talk shop’

Article 6 Piloting: State of Play and Stakeholder Experiences

This report is the 3rd edition of a series started in 2019 and provides an updated overview of all aspects related to the piloting and operationalization of Article 6 of the Paris Agreement. Despite the continued uncertainty regarding the finalization of the Article 6 rules, practical Article 6 piloting is continuing apace and the landscape of Article 6 piloting initiatives evolves. Testing how Article 6 cooperation could work in practice in order to inform negotiations as well as getting early access to sources of emissions credits is seen as important to fulfill national mitigation commitments. As a framework for the analysis in our study, we apply a ‘concentric ring’ model that clearly differentiates between piloting activities that aim at generating Internationally Transferred Mitigation Outcomes (ITMOs) or adaptation benefits (ABs), initiatives that will eventually be governed by Article 6 rules and the enabling environment, which is essential to drive piloting efforts forward. In an additional analytical step, we classify piloting activities in the inner circle according to three different phases: the preparatory phase, the pilot phase and the full implementation phase. Moreover, we summarise current stakeholder experiences with Article 6 piloting and provide an overview of our insights from broad and deep stakeholder consultations, including the views of buyer countries, host countries and project developers

Deploying and Evaluating Pufferfish Privacy for Smart Meter Data (Technical Report \u2715)

Information hiding ensures privacy by transforming personalized data so that certain sensitive information cannot be inferred any more. One state-of-the-art information-hiding approach is the Pufferfish framework. It lets the users specify their privacy requirements as so-called discriminative pairs of secrets, and it perturbs data so that an adversary does not learn about the probability distribution of such pairs. However, deploying the framework on complex data such as time series requires application specific work. This includes a general definition of the representation of secrets in the data. Another issue is that the tradeoff between Pufferfish privacy and utility of the data is largely unexplored in quantitative terms. In this study, we quantify this tradeoff for smart meter data. Such data contains fine-grained time series of power-consumption data from private households. Disseminating such data in an uncontrolled way puts privacy at risk. We investigate how time series of energy consumption data must be transformed to facilitate specifying secrets that Pufferfish can use. We ensure the generality of our study by looking at different information-extraction approaches, such as re-identification and non-intrusive-appliance-load monitoring, in combination with a comprehensive set of secrets. Additionally, we provide quantitative utility results for a real-world application, the so-called local energy market

Thromboelastometry for the assessment of coagulation abnormalities in early and established adult sepsis: a prospective cohort study

INTRODUCTION: The inflammatory response to an invading pathogen in sepsis leads to complex alterations in hemostasis by dysregulation of procoagulant and anticoagulant factors. Recent treatment options to correct these abnormalities in patients with sepsis and organ dysfunction have yielded conflicting results. Using thromboelastometry (ROTEM(R)), we assessed the course of hemostatic alterations in patients with sepsis and related these alterations to the severity of organ dysfunction. METHODS: This prospective cohort study included 30 consecutive critically ill patients with sepsis admitted to a 30-bed multidisciplinary intensive care unit (ICU). Hemostasis was analyzed with routine clotting tests as well as thromboelastometry every 12 hours for the first 48 hours, and at discharge from the ICU. Organ dysfunction was quantified using the Sequential Organ Failure Assessment (SOFA) score. RESULTS: Simplified Acute Physiology Score II and SOFA scores at ICU admission were 52 +/- 15 and 9 +/- 4, respectively. During the ICU stay the clotting time decreased from 65 +/- 8 seconds to 57 +/- 5 seconds (P = 0.021) and clot formation time (CFT) from 97 +/- 63 seconds to 63 +/- 31 seconds (P = 0.017), whereas maximal clot firmness (MCF) increased from 62 +/- 11 mm to 67 +/- 9 mm (P = 0.035). Classification by SOFA score revealed that CFT was slower (P = 0.017) and MCF weaker (P = 0.005) in patients with more severe organ failure (SOFA >or= 10, CFT 125 +/- 76 seconds, and MCF 57 +/- 11 mm) as compared with patients who had lower SOFA scores (SOFA <10, CFT 69 +/- 27, and MCF 68 +/- 8). Along with increasing coagulation factor activity, the initially increased International Normalized Ratio (INR) and prolonged activated partial thromboplastin time (aPTT) corrected over time. CONCLUSIONS: Key variables of ROTEM(R) remained within the reference ranges during the phase of critical illness in this cohort of patients with severe sepsis and septic shock without bleeding complications. Improved organ dysfunction upon discharge from the ICU was associated with shortened coagulation time, accelerated clot formation, and increased firmness of the formed blood clot when compared with values on admission. With increased severity of illness, changes of ROTEM(R) variables were more pronounced