Mitigating Branch-Shadowing Attacks on Intel SGX using Control Flow Randomization

Intel Software Guard Extensions (SGX) is a promising hardware-based technology for protecting sensitive computations from potentially compromised system software. However, recent research has shown that SGX is vulnerable to branch-shadowing -- a side channel attack that leaks the fine-grained (branch granularity) control flow of an enclave (SGX protected code), potentially revealing sensitive data to the attacker. The previously-proposed defense mechanism, called Zigzagger, attempted to hide the control flow, but has been shown to be ineffective if the attacker can single-step through the enclave using the recent SGX-Step framework. Taking into account these stronger attacker capabilities, we propose a new defense against branch-shadowing, based on control flow randomization. Our scheme is inspired by Zigzagger, but provides quantifiable security guarantees with respect to a tunable security parameter. Specifically, we eliminate conditional branches and hide the targets of unconditional branches using a combination of compile-time modifications and run-time code randomization. We evaluated the performance of our approach by measuring the run-time overhead of ten benchmark programs of SGX-Nbench in SGX environment

Consumption patterns and living conditions inside Het Steen, the late medieval prison of Malines (Mechelen, Belgium)

Excavations at the Main Square (Grote Markt) of Malines (Mechelen, Belgium) have unearthed the building remains of a tower, arguably identifiable as the former town prison: Het Steen. When this assumption is followed, the contents of the fills of two cesspits dug out in the cellars of the building illustrate aspects of daily life within the early 14th-century prison. An integrated approach of all find categories, together with the historical context available, illuminates aspects of the material culture of the users of the cesspits, their consumption patterns and the living conditions within the building

Identifying client characteristics to predict homecare use more accurately:a Delphi-study involving nurses and homecare purchasing specialists

BackgroundCase-mix based prospective payment of homecare is being implemented in several countries to work towards more efficient and client-centred homecare. However, existing models can only explain a limited part of variance in homecare use, due to their reliance on health- and function-related client data. It is unclear which predictors could improve predictive power of existing case-mix models. The aim of this study was therefore to identify relevant predictors of homecare use by utilizing the expertise of district nurses and health insurers.MethodsWe conducted a two-round Delphi-study according to the RAND/UCLA Appropriateness Method. In the first round, participants assessed the relevance of eleven client characteristics that are commonly included in existing case-mix models for predicting homecare use, using a 9-Point Likert scale. Furthermore, participants were also allowed to suggest missing characteristics that they considered relevant. These items were grouped and a selection of the most relevant items was made. In the second round, after an expert panel meeting, participants re-assessed relevance of pre-existing characteristics that were assessed uncertain and of eleven suggested client characteristics. In both rounds, median and inter-quartile ranges were calculated to determine relevance.ResultsTwenty-two participants (16 district nurses and 6 insurers) suggested 53 unique client characteristics (grouped from 142 characteristics initially). In the second round, relevance of the client characteristics was assessed by 12 nurses and 5 health insurers. Of a total of 22 characteristics, 10 client characteristics were assessed as being relevant and 12 as uncertain. None was found irrelevant for predicting homecare use. Most of the client characteristics from the category ‘Daily functioning’ were assessed as uncertain. Client characteristics in other categories – i.e. ‘Physical health status’, ‘Mental health status and behaviour’, ‘Health literacy’, ‘Social environment and network’, and ‘Other’ – were more frequently considered relevant.ConclusionAccording to district nurses and health insurers, homecare use could be predicted better by including other more holistic predictors in case-mix classification, such as on mental functioning and social network. The challenge remains, however, to operationalize the new characteristics and keep stakeholders on board when developing and implementing case-mix classification for homecare prospective payment