1,721,024 research outputs found
A protocol for metering data pseudonymization in smart grids
No full textA tradeoff between data collection needs and user privacy is of paramount importance in the Smart Grid. This paper proposes a pseudonymization protocol for data gathered by the Smart Metres, which relies on a network infrastructure and a dedicated set of nodes, called privacy preserving nodes. The network privacy is enforced by a separation of duties; the privacy preserving nodes perform data pseudonymization without having access to the measurements, which are masked by means of a secret sharing scheme, while the entities accessing the data recover and relate the plain measurements generated by the same metre along a time window of finite duration but have no access to the metre identities. The paper also provides an evaluation of the security and of the performance of the protocol, comparing it to the two alternative encryption techniques, which mask the measurements by means of the Chaum mixing scheme or of an identity-based proxy re-encryption scheme
Detection and mitigation of the eclipse attack in chord overlays
No full textDistributed Hash Table-based overlays are widely used to support efficient information routing and storage in structured peerto-
peer networks, but they are also subject to numerous attacks aimed at disrupting their correct functioning. In this paper we
analyze the impact of the Eclipse attack on a Chord-based overlay in terms of number of key lookups intercepted by a collusion
of malicious nodes. We propose a detection algorithm for the individuation of ongoing attacks to the Chord network, relying on
features that can be independently estimated by each network peer, which are given as input to a C4.5-based binary classifier.
Moreover, we propose some modifications to the Chord routing protocol in order to mitigate the effects of such attacks. The
countermeasures can operate in a distributed fashion or assume the presence of a centralized trusted entity and introduce a limited
traffic overhead. The effectiveness of the proposed mitigation techniques has been shown through numerical results
Internet Traffic Classification Using the Index of Variability
No full textInternet Traffic Classification aims at the identification of the Internet application that generates a given sequence of packets. Shallow Packet Inspection (SPI) is a new family of classification techniques that only use information available in the external header of packets and the statistical characterization of the traffic process. Therefore, these techniques are applicable even to encrypted or obfuscated traffic. The packet arrival process is a particularly interesting features for traffic classification, as it is difficult to significantly modify it. This paper proposes a classification technique based on a classification feature called Index of Variability, which evaluates the traffic source burstiness over various time scales in order to discriminate among different classes of Internet applications. Experimental results show that this classification method operates effectively both on synthetic and real traffic traces. Synthetic traffic traces make it possible to estimate the classification error rate achieved by the classification algorithm. The usage of real traces allows us to compare the performance of the method to the performance obtained with Deep Packet Inspection (DPI) techniques, showing that SPI and DPI yields similar results
Critical Node Identification for Cyber–Physical Power Distribution Systems Based on Complex Network Theory: A Real Case Study
Full text linkIn today’s world, power distribution systems and information and communication technology (ICT) systems are increasingly interconnected, forming cyber–physical power systems (CPPSs) at the core of smart grids. Ensuring the resilience of these systems is essential for maintaining reliable performance under disasters, failures, or cyber-attacks. Identifying critical nodes within these interdependent networks is key to preserving system robustness. This paper applies complex network (CN) theory—specifically degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC)—to a real-world distribution grid integrated with an ICT layer in northeastern Italy. Simulations are conducted across three scenarios: a directed power network, an undirected power network, and an undirected ICT network. Each centrality metric generates a ranking of nodes which is validated using node removal performance (NRP) analysis. In the directed power network, in-closeness centrality and out-degree centrality are the most effective in identifying critical nodes, with correlations of 84% and 74% with NRP, respectively. DC and BC perform best in the undirected power network, with correlation values of 67% and 53%, respectively. In the ICT network, BC achieves the highest correlation (64%), followed by CC at 55%. These findings demonstrate the potential of centrality-based methods for identifying critical nodes and support strategies for enhancing CPPS resilience and fault recovery by distribution system operators
Node Centrality Evaluation Based on Complex Network Theory: A Real Case Study for an Integrated Power Distribution and ICT System
No full textNowadays, power distribution systems and Information and Communication Technology (ICT) systems are interdependent onto each other, creating smart grids. The resilience of these integrated systems is crucial to ensure reliable performance against possible disasters and failures. To evaluate the most important components of the power grid and ICT system, complex network theory is one of the methods for modeling interdependent systems. This paper focuses on different centrality analyses for a real case study relevant to a power distribution network integrated with an ICT network. The simulation results demonstrate how centrality analysis can help identify which nodes of the power grid and the ICT network are critical for the resilience performance of the overall system
Privacy-Friendly Load Scheduling of Deferrable and Interruptible Domestic Appliances in Smart Grids
Full text linkThe massive integration of renewable energy sources in the power grid ecosystem
with the aim of reducing carbon emissions must cope with their intrinsically
intermittent and unpredictable nature. Therefore, the grid must improve its
capability of controlling the energy demand by adapting the power consumption
curve to match the trend of green energy generation. This could be done by
scheduling the activities of deferrable and/or interruptible electrical appliances.
However, communicating the users' needs about the usage of their appliances
also leaks sensitive information about their habits and lifestyles, thus arising
privacy concerns.
This paper proposes a framework to allow the coordination of energy consumption
without compromising the privacy of the users: the service requests
generated by the domestic appliances are divided into crypto-shares using Shamir
Secret Sharing scheme and collected through an anonymous routing protocol by
a set of schedulers, which schedule the requests by directly operating on the
shares. We discuss the security guarantees provided by our proposed infrastructure
and evaluate its performance, comparing it with the optimal scheduling
obtained by means of an Integer Linear Programming formulation
Privacy-Preserving Multi-Operator Contact Tracing for Early Detection of Covid19 Contagions
Full text linkThe outbreak of coronavirus disease 2019 (Covid-19) is imposing a severe worldwide lock-down. Contact tracing based on smartphones' applications (apps) has emerged as a possible solution to trace contagions and enforce a more sustainable selective quarantine. However, a massive adoption of these apps is required to reach the critical mass needed for effective contact tracing. As an alternative, geo-location technologies in next generation networks (e.g., 5G) can enable Mobile Operators (MOs) to perform passive tracing of users' mobility and contacts with a promised accuracy of down to one meter. To effectively detect contagions, the identities of positive individuals, which are known only by a Governmental Authority (GA), are also required. Note that, besides being extremely sensitive, these data might also be critical from a business perspective. Hence, MOs and the GA need to exchange and process users' geo-locations and infection status data in a privacy-preserving manner. In this work, we propose a privacy-preserving protocol that enables multiple MOs and the GA to share and process users' data to make only the final users discover the number of their contacts with positive individuals. The protocol is based on existing privacy-enhancing strategies that guarantee that users' mobility and infection status are only known to their MOs and to the GA, respectively. From extensive simulations, we observe that the cost to guarantee total privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable, and can also be significantly reduced if we accept a negligible compromise in users' privacy
Modelling spectrum assignment in a two-service flexi-grid optical link with imprecise continuous-time Markov chains
No full textThe possibility of flexibly assigning spectrum resources with channels of different sizes greatly improves the spectral efficiency of optical networks. The drawback of this flexibility is the risk of spectrum fragmentation. We study this problem in the two-service scenario. Our first contribution consists of exact Markov models for different assignment policies. Since these exact models do not scale to large systems, we then extend an approximate, reduced-state model that is available in the literature. In addition, we introduce a Markov model that uses imprecise probabilities, which allows us to derive upper and lowerboundsonblockingprobabilitieswithoutneedingtospecify an assignment policy. The obtained imprecise Markov chain can be used to evaluate the precision of approximate reduced-state models as well as to provide policy-free performance bounds
Secure and Differentially Private Detection of Net Neutrality Violations by Means of Crowdsourced Measurements
Full text linkEvaluating Network Neutrality requires comparing the quality of service experienced by multiple users served by different Internet Service Providers. Consequently, the issue of guaranteeing privacy-friendly network measurements has recently gained increasing interest. In this paper we propose a system which gathers throughput measurements from users of various applications and Internet services and stores it in a crowdsourced database, which can be queried by the users themselves to verify if their submitted measurements are compliant with the hypothesis of a neutral network. Since the crowdsourced data may disclose sensitive information about users and their habits, thus leading to potential privacy leakages, we adopt a privacy-preserving method based on randomized sampling and suppression of small clusters. Numerical results show that the proposed solution ensures a good trade-off between usefulness of the system, in terms of precision and recall of discriminated users, and privacy, in terms of differential privacy
To be neutral or not neutral? the in-network caching dilemma
Full text linkCaching allows Internet Service Providers (ISPs) to reduce network traffic and Content Providers (CPs) to increase the offered QoS. However, when contents are encrypted, effective caching is possible only if ISPs and CPs cooperate. We suggest possible forms of non-discriminatory cooperation that make caching compliant with the principles of Net-Neutrality (NN
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