These Walls Can Talk! Securing Digital Privacy in the Smart Home Under the Fourth Amendment

Privacy law in the United States has not kept pace with the realities of technological development, nor the growing reliance on the Internet of Things (IoT). As of now, the law has not adequately secured the “smart” home from intrusion by the state, and the Supreme Court further eroded digital privacy by conflating the common law concepts of trespass and exclusion in United States v. Jones. This article argues that the Court must correct this misstep by explicitly recognizing the method by which the Founding Fathers sought to “secure” houses and effects under the Fourth Amendment. Namely, the Court must reject its overly narrow trespass approach in lieu of the more appropriate right to exclude. This will better account for twenty-first century surveillance capabilities and properly constrain the state. Moreover, an exclusion framework will bolster the reasonable expectation of digital privacy by presuming an objective unreasonableness in any warrantless penetration by the state into the smart home

Online Privacy Control Via Anonymity And Pseudonym: Cross-Cultural Implications

Privacy’s exact nature needs to re?ect the contemporary view of a society. A growing number of online users demand the protection of their personal privacy via anonymity and pseudonym. The e?cacy of these two privacy controls in di?erent online environments is unknown. This study applies social psychology theories to explore the relationship between these personal sentiments—authoritative personality, empathy, fear of negative evaluation, self-esteem, and motives of online privacy rights. We conducted a quasi-experiment by manipulating four online environments (personal e-mail exchange, members-only newsgroup, public newsgroup, and online chat room), and three user identi?cation modes (real name, anonymity and pseudonym). More than 600 subjects from the USA and Taiwan participated in the experimental study. The results of path analysis con?rm the e?ects of some personal sentiments on the motives of online privacy rights. The study concludes with theoretical and practical implications for the roles of privacy in the online society

Modeling operating system crash behavior through multifractal analysis, long range dependence and mining of memory usage patterns

Software Aging is a phenomenon where the state of the operating systems degrades over a period of time due to transient errors. These transient errors can result in resource exhaustion and operating system hangups or crashes.;Three different techniques from fractal geometry are studied using the same datasets for operating system crash modeling and prediction. Holder Exponent is an indicator of how chaotic a signal is. M5 Prime is a nominal classification algorithm that allows prediction of a numerical quantity such as time to crash based on current and previous data. Hurst exponent measures the self similarity and long range dependence or memory of a process or data set and has been used to predict river flows and network usage.;For each of these techniques, a thorough investigation was conducted using crash, hangup and nominal operating system monitoring data. All three approaches demonstrated a promising ability to identify software aging and predict upcoming operating system crashes. This thesis describes the experiments, reports the best candidate techniques and identifies the topics for further investigation

Rejuvenation and the Age of Information

International audienc

Perspectives on the sources of heterogeneity in Indian industry

The authors examine technical efficiency variation across four industrial sectors in India, using a stochastic production frontier technique. The results are comparable to technical efficiency distribution patterns obtained in other countries. The authors examine heterogeneity in firm-level efficiency against internal, firm-level characteristics and against external characteristics (industry and location). The results suggest that managerial effectiveness significantly influences efficiency and that considerable benefits derive from location within established industrial clusters for particular industries. The methodology and findings indicate that the study of industry-specific technical efficiency patterns is a useful analytical tool for tracking domestic firms'response to liberalization and the advance of market forces. An important policy implication of the authors'results: There is considerable room for efficiency gains through better organization and management of production processes and improved supply chain management, even in the highly organized corporate sector. These gains could be achieved by purely internal learning processes with no extra investment in physical plant or equipment, or with the help of outside consultants, or through business alliances with partners from industrial countries (a rising trend). The results also show that greater technical efficiency correlates with better energy use and higher investments in plant management. How firms can be induced to undertake such investments in the"software"of production is an important issue. Liberalization and globalization are likely to bring significant productivity gains even in low-technology industries as managers gear up to meet the challenges of competition.Environmental Economics&Policies,Water and Industry,Health Monitoring&Evaluation,Economic Theory&Research,Banks&Banking Reform

Near-optimal scheduling and decision-making models for reactive and proactive fault tolerance mechanisms

As High Performance Computing (HPC) systems increase in size to fulfill computational power demand, the chance of failure occurrences dramatically increases, resulting in potentially large amounts of lost computing time. Fault Tolerance (FT) mechanisms aim to mitigate the impact of failure occurrences to the running applications. However, the overhead of FT mechanisms increases proportionally to the HPC systems\u27 size. Therefore, challenges arise in handling the expensive overhead of FT mechanisms while minimizing the large amount of lost computing time due to failure occurrences. In this dissertation, a near-optimal scheduling model is built to determine when to invoke a hybrid checkpoint mechanism, by means of stochastic processes and calculus of variations. The obtained schedule minimizes the waste time caused by checkpoint mechanism and failure occurrences. Generally, the checkpoint/restart mechanisms periodically save application states and load the saved state, upon failure occurrences. Furthermore, to handle various FT mechanisms, an adaptive decision-making model has been developed to determine the best FT strategy to invoke at each decision point. The best mechanism at each decision point is selected among considered FT mechanisms to globally minimize the total waste time for an application execution by means of a dynamic programming approach. In addition, the model is adaptive to deal with changes in failure rate over time