Experimental Pitfalls Clause Samples

Experimental Pitfalls. The effective and efficient evaluation of the actual mask order of cryptographic implementations remains an open problem due to several evaluation pitfalls. Effectivity-wise, when evaluating a masking scheme via the measured power consumption, we face the pitfall of the limited attack scope. That is, a particular attack technique in use may fail to exploit the available leakage due to e.g. an unsuitable choice of intermediate values or an incorrect power model assumption7. Moreover, introducing additional countermeasures on top of the masking scheme may render particular exploitation techniques ineffective, while the implementation remains vulnerable to different lines of attack. In order to tackle this issue, the research community followed several approaches. Prior research established generic side-channel distinguishers such as Mutual Information Analysis (MIA) [4], the Kolmogorov-▇▇▇▇▇▇▇ and the Cr`amer-von Mises tests [48, 49], which require minimal assumptions about the noise and the power model of the device under test. On the other side of the spectrum, ▇▇▇▇▇▇▇▇▇ et al. [44] proposed an evaluation framework assuming the strongest possible adversary, equipped with extensive profiling capabilities and Bayesian templates. While being effective, the aforementioned approaches focus on leakage exploitation and perform key recovery, which may require a large number of traces. Thus, they face the efficiency pitfall w.r.t. computational and storage requirements. Note that this increased demand for resources is magnified when inserting extra countermeasures in a masked implementation. Thus, it can be difficult to decide with confidence whether the masking order is reduced or not. In order to evaluate the effective masking order, we opt for a more recent app- ▇▇▇▇▇ called leakage detection methodology [31]. This approach focuses on leakage detection and disregards exploitation. Thus, the acquisition and the computa- tional cost is reduced while the methodology can retain its generic nature. Despite the gain achieved via decoupling detection and exploitation, the leak- age detection methodology still presents challenges w.r.t. efficiency. In the con- text of software masking, we need to combine multiple time samples in order to evaluate the masked implementation. Thus, we rely on the work by ▇▇▇▇▇▇▇▇▇ et al. [42], who extended the leakage detection methodology into higher-order evaluations by providing efficient, incremental formulas that can handle the com- putation involved wi...
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Experimental Pitfalls. The effective and efficient evaluation of the actual mask order of cryptographic implementations remains an open problem due to several evaluation pitfalls. Effectivity-wise, when evaluating a masking scheme via the measured power consumption, we face the pitfall of the limited attack scope. That is, a particular attack technique in use may fail to exploit the available leakage due to e.g. an unsuitable choice of intermediate values or an incorrect power model assumption7. Moreover, introducing additional countermeasures on top of the masking scheme may render particular exploitation techniques ineffective, while the implementation remains vulnerable to different lines of attack. In order to tackle this issue, the research community followed several approaches. Prior research established generic side-channel distinguishers such as Mutual Information Analysis (MIA) [4], the Kolmogorov-▇▇▇▇▇▇▇ and the Cr`amer-von Mises tests [48, 49], which require minimal assumptions about the noise and the power model of the device under test. On the other side of the spectrum, ▇▇▇▇▇▇▇▇▇ et al. [44] proposed an evaluation framework assuming the strongest possible adversary, equipped with extensive profiling capabilities and Bayesian templates.
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Related to Experimental Pitfalls

  • Study An application for leave of absence for professional study must be supported by a written statement indicating what study or research is to be undertaken, or, if applicable, what subjects are to be studied and at what institutions.

  • Research Primary Investigator as part of a multi-site study (25 points) • Co-Investigator as part of a multi-site study (20 points) • Primary Investigator of a facility/unit based research study (15 points) • Co-Investigator of a facility/unit based research study (10 points) • Develops a unit specific research proposal (5 points) • Conducts a literature review as part of a research study (5 points)

  • Clinical 1.1 Provides comprehensive evidence based nursing care and individual case management to a specific group of patients/clients including assessment, intervention and evaluation. 1.2 Undertakes clinical shifts at the direction of senior staff and the Nursing Director including participation on the on-call/after-hours/weekend roster if required. 1.3 Responsible and accountable for patient safety and quality of care through planning, coordinating, performing, facilitating, and evaluating the delivery of patient care relating to a particular group of patients, clients or staff in the practice setting. 1.4 Monitors, reviews and reports upon the standard of nursing practice to ensure that colleagues are working within the scope of nursing practice, following appropriate clinical pathways, policies, procedures and adopting a risk management approach in patient care delivery. 1.5 Participates in ▇▇▇▇ rounds/case conferences as appropriate. 1.6 Educates patients/carers in post discharge management and organises discharge summaries/referrals to other services, as appropriate. 1.7 Supports and liaises with patients, carers, colleagues, medical, nursing, allied health, support staff, external agencies and the private sector to provide coordinated multidisciplinary care. 1.8 Completes clinical documentation and undertakes other administrative/management tasks as required. 1.9 Participates in departmental and other meetings as required to meet organisational and service objectives. 1.10 Develops and seeks to implement change utilising expert clinical knowledge through research and evidence based best practice. 1.11 Monitors and maintains availability of consumable stock. 1.12 Complies with and demonstrates a positive commitment to Regulations, Acts and Policies relevant to nursing including the Code of Ethics for Nurses in Australia, the Code of Conduct for Nurses in Australia, the National Competency Standards for the Registered Nurse and the Poisons Act 2014 and Medicines and Poisons Regulations 2016. 1.13 Promotes and participates in team building and decision making. 1.14 Responsible for the clinical supervision of nurses at Level 1 and/or Enrolled Nurses/ Assistants in Nursing under their supervision.

  • Development Within twenty (20) Working Days after the Commencement Date and in accordance with paragraphs 3.10 to 3.12 (Amendment and Revision), the Contractor will prepare and deliver to the Authority for approval the full and final Security Plan which will be based on the draft Security Plan set out in Appendix B.

  • Diagnostic procedures to aid the Provider in determining required dental treatment.