Georgios Kalogridis - Academia.edu (original) (raw)

Papers by Georgios Kalogridis

arXiv (Cornell University), Feb 4, 2016

The connectivity properties of ad hoc networks have been extensively studied over the past few ye... more The connectivity properties of ad hoc networks have been extensively studied over the past few years, from local observables, to global network properties. In this paper we introduce a novel layer of network dynamics which lives and evolves on top of the ad hoc network. Nodes are assumed selfish and a snowdrift type game is defined dictating the way nodes decide to allocate their cooperative resource efforts towards other nodes in the network. The dynamics are strongly coupled with the physical network causing the cooperation network topology to converge towards a stable equilibrium state, a global maximum of the total pay-off. We study this convergence from a connectivity perspective and analyse the inherent parameter dependence. Moreover, we show that direct reciprocity can be an efficient incentive to promote cooperation within the network and discuss the analogies between our simple yet tractable framework with D2D proximity based services such as LTE-Direct. We argue that cooperative network dynamics have many application in ICT, not just ad hoc networks, and similar models as the one described herein can be devised and studied in their own right.

Although dataset shift is normally assumed to occur between the training context and the deployme... more Although dataset shift is normally assumed to occur between the training context and the deployment context, it can also be found within a large training dataset. In this paper we address the problem of detecting dataset shift with the Subgroup Discovery scheme. Instead of treating the training data as a single context or a set of fixed sub-contexts, we use Subgroup Discovery to locate subcontexts with both large coverage and significant dataset shifts. Models can then be trained specifically for these sub-contexts and deployed to the corresponding testing data. We also propose a variation of Subgroup Discovery, called Model-Based Subgroup Discovery, to detect a richer form of dataset shifts effectively.

arXiv (Cornell University), Apr 20, 2023

Delays in activities completion drive human projects to schedule and cost overruns. It is believe... more Delays in activities completion drive human projects to schedule and cost overruns. It is believed activity delays are the consequence of multiple idiosyncrasies without specific patterns or rules. Here we show that is not the case. Using data for 180 construction project schedules, we demonstrate that activity delays satisfy a universal model that we call the law of activity delays. After we correct for delay risk factors, what remains follows a log-normal distribution.

Scientific Reports, Jan 10, 2023

Projects are characterised by activity networks with a critical path, a sequence of activities fr... more Projects are characterised by activity networks with a critical path, a sequence of activities from start to end, that must be finished on time to complete the project on time. Watching over the critical path is the project manager's strategy to ensure timely project completion. This intense focus on a single path contrasts the broader complex structure of the activity network, and is due to our poor understanding on how that structure influences this critical path. Here, we use a generative model and detailed data from 77 real world projects (+ 10bntotalbudget)todemonstratehowthisnetworkstructureforcesustolookbeyondthecriticalpath.Weintroduceaduplication−splitmodelofprojectschedulesthatyields(i)identicalpower−lawin−and−outdegreedistributionsand(ii)avanishingfractionofcriticalpathactivitieswithschedulesize.Thesepredictionsarecorroboratedinrealprojects.Wedemonstratethattheincidenceofdelayedactivitiesinrealprojectsisconsistentwiththeexpectationfrompercolationtheoryincomplexnetworks.Weconcludethatdelaypropagationinprojectschedulesisanetworkpropertyanditisnotconfinedtothecriticalpath.Deliveringprojectsontimeandonbudgetisnecessarytoimprovehumanprospect1,withtheWorldBankstatingthat2210 bn total budget) to demonstrate how this network structure forces us to look beyond the critical path. We introduce a duplication-split model of project schedules that yields (i) identical power-law in-and-out degree distributions and (ii) a vanishing fraction of critical path activities with schedule size. These predictions are corroborated in real projects. We demonstrate that the incidence of delayed activities in real projects is consistent with the expectation from percolation theory in complex networks. We conclude that delay propagation in project schedules is a network property and it is not confined to the critical path. Delivering projects on time and on budget is necessary to improve human prospect 1 , with the World Bank stating that 22% of the world's gross domestic product-about 10bntotalbudget)todemonstratehowthisnetworkstructureforcesustolookbeyondthecriticalpath.Weintroduceaduplication−splitmodelofprojectschedulesthatyields(i)identicalpower−lawin−and−outdegreedistributionsand(ii)avanishingfractionofcriticalpathactivitieswithschedulesize.Thesepredictionsarecorroboratedinrealprojects.Wedemonstratethattheincidenceofdelayedactivitiesinrealprojectsisconsistentwiththeexpectationfrompercolationtheoryincomplexnetworks.Weconcludethatdelaypropagationinprojectschedulesisanetworkpropertyanditisnotconfinedtothecriticalpath.Deliveringprojectsontimeandonbudgetisnecessarytoimprovehumanprospect1,withtheWorldBankstatingthat2248 trillion-relies exclusively on project-based delivery mechanisms 2. Yet the majority of public and private large-capital projects are completed late and over budget 3. An industry survey captures the scale of the problem-reviewing 10,624 projects from 200 companies in 30 countries and across a variety of industries, it concludes that only 2.5% of projects were completed on time and budget 4. A recent review reaffirms the stubbornness of the challenge, with delays remaining at comparable levels even after 15 years of project management advancements (comparing projects started between 1998 and 2003 vs. 2013-2018) 4. This consistency in poor performance suggests that the core method of evaluating delay risk is inadequate for the complex nature of modern projects. Known risk events can be identified, analyzed and responded using risk management plans during the planning phase. However, unknown risk events deteriorate the project performance. Since the 1960s project managers have almost exclusively relied on monitoring the critical path as the means to manage delay risk. This path is essentially a sequence of activities from start to end that are executed without any slack time in between 5,6. The critical path activities play a key role in the scheduling of limited resources and the delay risk analysis. An increase in the duration of any activity in the critical path causes project end overrun. It is a simple concept and it provides a simple solution: the critical path must be executed as planned at all costs. Yet, modern projects are more complex, with schedules that look like complex networks of activity dependencies 7,8. Delays in activities outside the critical path can similarly cause project end overruns through domino-like cascades, similar to how viruses spread 9. Given the consistency in project delays over the past decades, we examine the limit of applicability for the critical path using both synthetic and real data. We find that, beyond a certain level of complexity, the critical path becomes irrelevant and project end overruns are primarily driven by activities that are outside of that path. Results Generative model of project schedules. A project schedule is generated using a standardised procedure. In that process planners take into account the state of the art of contractors operations. If specialization occurs and the work of a former contractor doing activity A is now carried on by two contractors doing activities A1 and A2, then we would experience a change of A to A1 and A2 when looking at schedules before and after this specialization. The evolution of project schedules (or activity networks) in time can be seen as the outcome of a growth process, where a parent activity can be duplicated or split (Fig. 1A). Generic activities can be duplicated and

Lecture Notes in Computer Science, 2016

Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given... more Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given population that have unusual distributional characteristics with regard to some target attribute. Such subgroups can be used as a statistical summary which improves on the default summary of stating the overall distribution in the population. A natural way to evaluate such summaries is to quantify the difference between predicted and empirical distribution of the target. In this paper we propose to use proper scoring rules, a well-known family of evaluation measures for assessing the goodness of probability estimators, to obtain theoretically well-founded evaluation measures for subgroup discovery. From this perspective, one subgroup is better than another if it has lower divergence of target probability estimates from the actual labels on average. We demonstrate empirically on both synthetic and real-world data that this leads to higher quality statistical summaries than the existing methods based on measures such as Weighted Relative Accuracy.

ABSTRACT In this paper we explore the possibility of inferring activities of daily life (ADLs) fr... more ABSTRACT In this paper we explore the possibility of inferring activities of daily life (ADLs) from aggregate power load signatures of people's homes, which has many applications including e-healthcare. Such power load data are available from smart meters that will be widely deployed in many countries by utilities or customers, creating an infrastructure at the forefront of the Internet of Things (IoT). The main contribution of this work is a time-dependent factorial hidden Markov model to extract behaviour related features linked with individual appliance usage. The results show that the introduced time-dependent structure can improve the performance while also provide a probability distribution related to ADLs. These results further provide a promising indication of appliance usage connotations of e-health, and a foundation for further research.

International Journal of Security and Networks, 2011

... In Figure 2(b), the appliance demand is covered by a mix of battery and utility energy. ... I... more ... In Figure 2(b), the appliance demand is covered by a mix of battery and utility energy. ... In this paper we wish to evaluate the performance of the privacy algorithm in Section 5.2, or ... Thus, we consider that we can evaluate the probability of the correct estimation of E (ie, successful ...

Applied Network Science, Mar 22, 2021

Spreading broadly refers to the notion of an entity propagating through a networked system, typic... more Spreading broadly refers to the notion of an entity propagating through a networked system, typically fueled by a dynamical process (Pastor-Satorras et al. 2015). Spreading processes are a powerful set of tools for modelling a wide-range of real-world phenomena, including the dissemination of (dis)information on social media (Vosoughi et al. 2018), the propagation of a pathogen within a population (Colizza et al. 2006), cyber attacks on computer networks (Cohen et al. 2003) and delays in transportation systems (Preciado et al. 2014). Node degree (Wasserman et al. 1994), betweenness centrality (Freeman 1977) and eigenvector centrality (Bonacich 1972) are all examples of topological metrics used to approximate the role of individual nodes in the context of spreading processes, a problem that yet remains open in the extant literature (Radicchi and Castellano 2016; Erkol et al. 2018). The problem is further complicated by the scarcity of reliable ground truth. Datasets providing an individual-level description of a spreading process within a population are

Who wants smart meter data? How could the data be used? Utilities To monitor electricity usage an... more Who wants smart meter data? How could the data be used? Utilities To monitor electricity usage and load; to determine bills Advisory companies To promote energy conservation and awareness Insurance companies To determine premiums based on unusual behaviors that might indicate illness Marketers To profile customers for targeted advertisements Law enforcers To identify suspicious or illegal activity Civil litigators To identify property boundaries and activities on premises Landlords To verify lease compliance Private investigators To monitor specific events The press To get information about famous people Creditors To determine behavior that might indicate creditworthiness Criminals To identify best times for a burglary, or Metrics 24/100

Security and Management, 2005

ACM transactions on computing for healthcare, Dec 30, 2020

Automated mood recognition has been studied in recent times with great emphasis on stress in part... more Automated mood recognition has been studied in recent times with great emphasis on stress in particular. Other affective states are also of great importance, as studying them can help in understanding human behaviours in more detail. Most of the studies conducted in the realisation of an automated system that is capable of recognising human moods have established that mood is personal—that is, mood perception differs amongst individuals. Previous machine learning--based frameworks confirm this hypothesis, with personalised models almost always outperforming the generalised methods. In this article, we propose a novel system for grouping individuals in what we refer to as “perception clusters” based on their physiological signals. We evaluate perception clusters with a trial of nine users in a work environment, recording physiological and activity data for at least 10 days. Our results reveal no significant difference in performance with respect to a personalised approach and that our method performs equally better against traditional generalised methods. Such an approach significantly reduces computational requirements that are otherwise necessary for personalised approaches requiring individual models developed separately for each user. Further, perception clusters manifest a direction towards semi-supervised affective modelling in which individual perceptions are inferred from the data.

The societal need for better public healthcare calls for granular, continuous, nationwide instrum... more The societal need for better public healthcare calls for granular, continuous, nationwide instrumentation and data fusion technologies. However, the current trend of centralised (database) health analytics gives rise to data privacy issues. This paper proposes sensor data mining algorithms that help infer health/well-being related lifestyle patterns and anomalous (or privacy-sensitive) events. Such algorithms enable a user-centric context awareness at the network edge, which can be used for decentralised eHealth decision making and privacy protection by design. The main hypothesis of this work involves the detection of atypical behaviours from a given stream of energy consumption data recorded at eight houses over a period of a year for cooking, microwave, and TV activities. Our initial exploratory results suggest that in the case of an unemployed single resident, the dayby-day variability of TV or microwave operation, in conjunction with the variability of the absence of other cooking activity, is more significant as compared with the variability of other combinations of activities. The proposed methodology brings together appliance monitoring, privacy, and anomaly detection within a healthcare context, which is readily scalable to include other health-related sensor streams.

In this paper, we propose a secure roaming electric vehicle (EV) charging protocol that helps pre... more In this paper, we propose a secure roaming electric vehicle (EV) charging protocol that helps preserve users' privacy. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user's contracted supplier) which is anonymously signed by the user's private key. This protocol protects the user's identity privacy from other suppliers as well as the user's privacy of location from its own supplier. Further, it allows the user's contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multiuser EV charging is supported and different legitimate EV users (e.g. family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the EV. On an application level, our protocol supports fair user billing, i.e. each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers.

IEEE Access, 2015

This paper proposes a novel solution, called a decentralized, efficient, privacy-preserving, and ... more This paper proposes a novel solution, called a decentralized, efficient, privacy-preserving, and selective aggregation (DEP2SA) scheme, designed to support secure and user privacy-preserving data collection in the advanced metering infrastructure. DEP2SA is more efficient and applicable in real-life deployment, as compared with the state of the art, by adopting and adapting a number of key technologies: 1) it uses a multi-recipient system model, making it more applicable to a liberalized electricity market; 2) it uses the homomorphic Paillier encryption and selective aggregation methods to protect users' consumption data against both external and internal attacks, thus making it more secure; 3) it aggregates data at the gateways that are closest to the data originator, thus saving bandwidth and reducing the risk of creating a performance bottleneck in the system; and 4) it uses short signature and batch signature verification methods to further reduce computational and communication overheads imposed on aggregating nodes. The scheme has been analyzed in terms of security, computational, and communication overheads, and the results show that it is more secure, efficient, and scalable than related schemes.

IEEE Transactions on Smart Grid, Dec 1, 2011

The data collected by a home smart meter can potentially reveal sensitive private information abo... more The data collected by a home smart meter can potentially reveal sensitive private information about the home resident(s). In this paper, we study how home energy resources can be used to protect the privacy of the collected data. In particular we: a) introduce a power mixing algorithm to selectively protect a set of consumption events; b) develop a range of different privacy protection metrics; c) analyze real smart metering data sampled twice a minute over a period of 13 days; and d) evaluate the protection offered by different power mixing algorithms. Major factors which determine the efficiency of the proposed power mixing algorithms are identified, such as battery capacity and power, and user preferences for privacy-based allocations of battery energy quotas.

A device and method for obtaining energy consumption data for a metered energy consumer, such as ... more A device and method for obtaining energy consumption data for a metered energy consumer, such as a household or business, anonymising by means of scrambling the obtained energy consumption data in a manner acceptable by the utility provider and reporting the anonymised energy consumption data to the utility provider. The device may be or form part of a smart meter. Utility data is modified in one embodiment such that the modified consumption data starts to converge with the obtained consumption data if a deviation of the obtained consumption from the modified consumption data exceeds a pre-determined threshold. In a second embodiment the modified consumption data is based on the obtained data and a convergence factor stored in the device. In a third embodiment the processor calculates the modified data so that it over-represents the consumed amount at one time and under represents it at other times so that at pre-determined times or after pre-determined periods the modified data rep...

arXiv (Cornell University), Feb 4, 2016

The connectivity properties of ad hoc networks have been extensively studied over the past few ye... more The connectivity properties of ad hoc networks have been extensively studied over the past few years, from local observables, to global network properties. In this paper we introduce a novel layer of network dynamics which lives and evolves on top of the ad hoc network. Nodes are assumed selfish and a snowdrift type game is defined dictating the way nodes decide to allocate their cooperative resource efforts towards other nodes in the network. The dynamics are strongly coupled with the physical network causing the cooperation network topology to converge towards a stable equilibrium state, a global maximum of the total pay-off. We study this convergence from a connectivity perspective and analyse the inherent parameter dependence. Moreover, we show that direct reciprocity can be an efficient incentive to promote cooperation within the network and discuss the analogies between our simple yet tractable framework with D2D proximity based services such as LTE-Direct. We argue that cooperative network dynamics have many application in ICT, not just ad hoc networks, and similar models as the one described herein can be devised and studied in their own right.

Although dataset shift is normally assumed to occur between the training context and the deployme... more Although dataset shift is normally assumed to occur between the training context and the deployment context, it can also be found within a large training dataset. In this paper we address the problem of detecting dataset shift with the Subgroup Discovery scheme. Instead of treating the training data as a single context or a set of fixed sub-contexts, we use Subgroup Discovery to locate subcontexts with both large coverage and significant dataset shifts. Models can then be trained specifically for these sub-contexts and deployed to the corresponding testing data. We also propose a variation of Subgroup Discovery, called Model-Based Subgroup Discovery, to detect a richer form of dataset shifts effectively.

arXiv (Cornell University), Apr 20, 2023

Delays in activities completion drive human projects to schedule and cost overruns. It is believe... more Delays in activities completion drive human projects to schedule and cost overruns. It is believed activity delays are the consequence of multiple idiosyncrasies without specific patterns or rules. Here we show that is not the case. Using data for 180 construction project schedules, we demonstrate that activity delays satisfy a universal model that we call the law of activity delays. After we correct for delay risk factors, what remains follows a log-normal distribution.

Scientific Reports, Jan 10, 2023

Projects are characterised by activity networks with a critical path, a sequence of activities fr... more Projects are characterised by activity networks with a critical path, a sequence of activities from start to end, that must be finished on time to complete the project on time. Watching over the critical path is the project manager's strategy to ensure timely project completion. This intense focus on a single path contrasts the broader complex structure of the activity network, and is due to our poor understanding on how that structure influences this critical path. Here, we use a generative model and detailed data from 77 real world projects (+ 10bntotalbudget)todemonstratehowthisnetworkstructureforcesustolookbeyondthecriticalpath.Weintroduceaduplication−splitmodelofprojectschedulesthatyields(i)identicalpower−lawin−and−outdegreedistributionsand(ii)avanishingfractionofcriticalpathactivitieswithschedulesize.Thesepredictionsarecorroboratedinrealprojects.Wedemonstratethattheincidenceofdelayedactivitiesinrealprojectsisconsistentwiththeexpectationfrompercolationtheoryincomplexnetworks.Weconcludethatdelaypropagationinprojectschedulesisanetworkpropertyanditisnotconfinedtothecriticalpath.Deliveringprojectsontimeandonbudgetisnecessarytoimprovehumanprospect1,withtheWorldBankstatingthat2210 bn total budget) to demonstrate how this network structure forces us to look beyond the critical path. We introduce a duplication-split model of project schedules that yields (i) identical power-law in-and-out degree distributions and (ii) a vanishing fraction of critical path activities with schedule size. These predictions are corroborated in real projects. We demonstrate that the incidence of delayed activities in real projects is consistent with the expectation from percolation theory in complex networks. We conclude that delay propagation in project schedules is a network property and it is not confined to the critical path. Delivering projects on time and on budget is necessary to improve human prospect 1 , with the World Bank stating that 22% of the world's gross domestic product-about 10bntotalbudget)todemonstratehowthisnetworkstructureforcesustolookbeyondthecriticalpath.Weintroduceaduplication−splitmodelofprojectschedulesthatyields(i)identicalpower−lawin−and−outdegreedistributionsand(ii)avanishingfractionofcriticalpathactivitieswithschedulesize.Thesepredictionsarecorroboratedinrealprojects.Wedemonstratethattheincidenceofdelayedactivitiesinrealprojectsisconsistentwiththeexpectationfrompercolationtheoryincomplexnetworks.Weconcludethatdelaypropagationinprojectschedulesisanetworkpropertyanditisnotconfinedtothecriticalpath.Deliveringprojectsontimeandonbudgetisnecessarytoimprovehumanprospect1,withtheWorldBankstatingthat2248 trillion-relies exclusively on project-based delivery mechanisms 2. Yet the majority of public and private large-capital projects are completed late and over budget 3. An industry survey captures the scale of the problem-reviewing 10,624 projects from 200 companies in 30 countries and across a variety of industries, it concludes that only 2.5% of projects were completed on time and budget 4. A recent review reaffirms the stubbornness of the challenge, with delays remaining at comparable levels even after 15 years of project management advancements (comparing projects started between 1998 and 2003 vs. 2013-2018) 4. This consistency in poor performance suggests that the core method of evaluating delay risk is inadequate for the complex nature of modern projects. Known risk events can be identified, analyzed and responded using risk management plans during the planning phase. However, unknown risk events deteriorate the project performance. Since the 1960s project managers have almost exclusively relied on monitoring the critical path as the means to manage delay risk. This path is essentially a sequence of activities from start to end that are executed without any slack time in between 5,6. The critical path activities play a key role in the scheduling of limited resources and the delay risk analysis. An increase in the duration of any activity in the critical path causes project end overrun. It is a simple concept and it provides a simple solution: the critical path must be executed as planned at all costs. Yet, modern projects are more complex, with schedules that look like complex networks of activity dependencies 7,8. Delays in activities outside the critical path can similarly cause project end overruns through domino-like cascades, similar to how viruses spread 9. Given the consistency in project delays over the past decades, we examine the limit of applicability for the critical path using both synthetic and real data. We find that, beyond a certain level of complexity, the critical path becomes irrelevant and project end overruns are primarily driven by activities that are outside of that path. Results Generative model of project schedules. A project schedule is generated using a standardised procedure. In that process planners take into account the state of the art of contractors operations. If specialization occurs and the work of a former contractor doing activity A is now carried on by two contractors doing activities A1 and A2, then we would experience a change of A to A1 and A2 when looking at schedules before and after this specialization. The evolution of project schedules (or activity networks) in time can be seen as the outcome of a growth process, where a parent activity can be duplicated or split (Fig. 1A). Generic activities can be duplicated and

Lecture Notes in Computer Science, 2016

Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given... more Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given population that have unusual distributional characteristics with regard to some target attribute. Such subgroups can be used as a statistical summary which improves on the default summary of stating the overall distribution in the population. A natural way to evaluate such summaries is to quantify the difference between predicted and empirical distribution of the target. In this paper we propose to use proper scoring rules, a well-known family of evaluation measures for assessing the goodness of probability estimators, to obtain theoretically well-founded evaluation measures for subgroup discovery. From this perspective, one subgroup is better than another if it has lower divergence of target probability estimates from the actual labels on average. We demonstrate empirically on both synthetic and real-world data that this leads to higher quality statistical summaries than the existing methods based on measures such as Weighted Relative Accuracy.

ABSTRACT In this paper we explore the possibility of inferring activities of daily life (ADLs) fr... more ABSTRACT In this paper we explore the possibility of inferring activities of daily life (ADLs) from aggregate power load signatures of people's homes, which has many applications including e-healthcare. Such power load data are available from smart meters that will be widely deployed in many countries by utilities or customers, creating an infrastructure at the forefront of the Internet of Things (IoT). The main contribution of this work is a time-dependent factorial hidden Markov model to extract behaviour related features linked with individual appliance usage. The results show that the introduced time-dependent structure can improve the performance while also provide a probability distribution related to ADLs. These results further provide a promising indication of appliance usage connotations of e-health, and a foundation for further research.

International Journal of Security and Networks, 2011

... In Figure 2(b), the appliance demand is covered by a mix of battery and utility energy. ... I... more ... In Figure 2(b), the appliance demand is covered by a mix of battery and utility energy. ... In this paper we wish to evaluate the performance of the privacy algorithm in Section 5.2, or ... Thus, we consider that we can evaluate the probability of the correct estimation of E (ie, successful ...

Applied Network Science, Mar 22, 2021

Spreading broadly refers to the notion of an entity propagating through a networked system, typic... more Spreading broadly refers to the notion of an entity propagating through a networked system, typically fueled by a dynamical process (Pastor-Satorras et al. 2015). Spreading processes are a powerful set of tools for modelling a wide-range of real-world phenomena, including the dissemination of (dis)information on social media (Vosoughi et al. 2018), the propagation of a pathogen within a population (Colizza et al. 2006), cyber attacks on computer networks (Cohen et al. 2003) and delays in transportation systems (Preciado et al. 2014). Node degree (Wasserman et al. 1994), betweenness centrality (Freeman 1977) and eigenvector centrality (Bonacich 1972) are all examples of topological metrics used to approximate the role of individual nodes in the context of spreading processes, a problem that yet remains open in the extant literature (Radicchi and Castellano 2016; Erkol et al. 2018). The problem is further complicated by the scarcity of reliable ground truth. Datasets providing an individual-level description of a spreading process within a population are

Who wants smart meter data? How could the data be used? Utilities To monitor electricity usage an... more Who wants smart meter data? How could the data be used? Utilities To monitor electricity usage and load; to determine bills Advisory companies To promote energy conservation and awareness Insurance companies To determine premiums based on unusual behaviors that might indicate illness Marketers To profile customers for targeted advertisements Law enforcers To identify suspicious or illegal activity Civil litigators To identify property boundaries and activities on premises Landlords To verify lease compliance Private investigators To monitor specific events The press To get information about famous people Creditors To determine behavior that might indicate creditworthiness Criminals To identify best times for a burglary, or Metrics 24/100

Security and Management, 2005

ACM transactions on computing for healthcare, Dec 30, 2020

Automated mood recognition has been studied in recent times with great emphasis on stress in part... more Automated mood recognition has been studied in recent times with great emphasis on stress in particular. Other affective states are also of great importance, as studying them can help in understanding human behaviours in more detail. Most of the studies conducted in the realisation of an automated system that is capable of recognising human moods have established that mood is personal—that is, mood perception differs amongst individuals. Previous machine learning--based frameworks confirm this hypothesis, with personalised models almost always outperforming the generalised methods. In this article, we propose a novel system for grouping individuals in what we refer to as “perception clusters” based on their physiological signals. We evaluate perception clusters with a trial of nine users in a work environment, recording physiological and activity data for at least 10 days. Our results reveal no significant difference in performance with respect to a personalised approach and that our method performs equally better against traditional generalised methods. Such an approach significantly reduces computational requirements that are otherwise necessary for personalised approaches requiring individual models developed separately for each user. Further, perception clusters manifest a direction towards semi-supervised affective modelling in which individual perceptions are inferred from the data.

The societal need for better public healthcare calls for granular, continuous, nationwide instrum... more The societal need for better public healthcare calls for granular, continuous, nationwide instrumentation and data fusion technologies. However, the current trend of centralised (database) health analytics gives rise to data privacy issues. This paper proposes sensor data mining algorithms that help infer health/well-being related lifestyle patterns and anomalous (or privacy-sensitive) events. Such algorithms enable a user-centric context awareness at the network edge, which can be used for decentralised eHealth decision making and privacy protection by design. The main hypothesis of this work involves the detection of atypical behaviours from a given stream of energy consumption data recorded at eight houses over a period of a year for cooking, microwave, and TV activities. Our initial exploratory results suggest that in the case of an unemployed single resident, the dayby-day variability of TV or microwave operation, in conjunction with the variability of the absence of other cooking activity, is more significant as compared with the variability of other combinations of activities. The proposed methodology brings together appliance monitoring, privacy, and anomaly detection within a healthcare context, which is readily scalable to include other health-related sensor streams.

In this paper, we propose a secure roaming electric vehicle (EV) charging protocol that helps pre... more In this paper, we propose a secure roaming electric vehicle (EV) charging protocol that helps preserve users' privacy. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user's contracted supplier) which is anonymously signed by the user's private key. This protocol protects the user's identity privacy from other suppliers as well as the user's privacy of location from its own supplier. Further, it allows the user's contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multiuser EV charging is supported and different legitimate EV users (e.g. family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the EV. On an application level, our protocol supports fair user billing, i.e. each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers.

IEEE Access, 2015

This paper proposes a novel solution, called a decentralized, efficient, privacy-preserving, and ... more This paper proposes a novel solution, called a decentralized, efficient, privacy-preserving, and selective aggregation (DEP2SA) scheme, designed to support secure and user privacy-preserving data collection in the advanced metering infrastructure. DEP2SA is more efficient and applicable in real-life deployment, as compared with the state of the art, by adopting and adapting a number of key technologies: 1) it uses a multi-recipient system model, making it more applicable to a liberalized electricity market; 2) it uses the homomorphic Paillier encryption and selective aggregation methods to protect users' consumption data against both external and internal attacks, thus making it more secure; 3) it aggregates data at the gateways that are closest to the data originator, thus saving bandwidth and reducing the risk of creating a performance bottleneck in the system; and 4) it uses short signature and batch signature verification methods to further reduce computational and communication overheads imposed on aggregating nodes. The scheme has been analyzed in terms of security, computational, and communication overheads, and the results show that it is more secure, efficient, and scalable than related schemes.

IEEE Transactions on Smart Grid, Dec 1, 2011

The data collected by a home smart meter can potentially reveal sensitive private information abo... more The data collected by a home smart meter can potentially reveal sensitive private information about the home resident(s). In this paper, we study how home energy resources can be used to protect the privacy of the collected data. In particular we: a) introduce a power mixing algorithm to selectively protect a set of consumption events; b) develop a range of different privacy protection metrics; c) analyze real smart metering data sampled twice a minute over a period of 13 days; and d) evaluate the protection offered by different power mixing algorithms. Major factors which determine the efficiency of the proposed power mixing algorithms are identified, such as battery capacity and power, and user preferences for privacy-based allocations of battery energy quotas.

A device and method for obtaining energy consumption data for a metered energy consumer, such as ... more A device and method for obtaining energy consumption data for a metered energy consumer, such as a household or business, anonymising by means of scrambling the obtained energy consumption data in a manner acceptable by the utility provider and reporting the anonymised energy consumption data to the utility provider. The device may be or form part of a smart meter. Utility data is modified in one embodiment such that the modified consumption data starts to converge with the obtained consumption data if a deviation of the obtained consumption from the modified consumption data exceeds a pre-determined threshold. In a second embodiment the modified consumption data is based on the obtained data and a convergence factor stored in the device. In a third embodiment the processor calculates the modified data so that it over-represents the consumed amount at one time and under represents it at other times so that at pre-determined times or after pre-determined periods the modified data rep...