arXiv subjects

Giulio Giaconi

Publications and source records attributed to Giulio Giaconi.

10 recordsLinked to original sources

arXiv2026

Online Fair Division Meets Reordering Buffers

We study the online fair division of indivisible mixed manna among agents with additive valuation functions. Under the standard online model, at each time step an indivisible item arrives; each agent may assign it a positive, negative, or zero value, and it must be irrevocably allocated, before the arrival of the next item. At the same time, we also wish to maintain some fairness guarantee, and in this work we focus on envy-freeness (EF) and one of its most prominent relaxations, envy-freeness up to one item (EF1). Given the strong negative and the scarce positive results for this problem without additional assumptions, we augment our algorithms with buffers that can store and rearrange a limited number of items. This setting interpolates naturally between the fully online case (no buffer) and the fully offline case (a buffer large enough to hold all items). We show that algorithms equipped with reasonably sized buffers can achieve strong guarantees for personalized $k$-value instances, i.e., instances in which each agent assigns at most $k$ distinct values to items. In particular, we construct allocations that are EF1 at every time step and EF at most time steps, using a buffer of size linear in $k$ and in the number of agents. Our approach relies on novel combinatorial arguments and on constructing a sequence of envy-free matchings that allocates most items. Finally, we extend our results to general additive valuation functions, with a dependence on the largest per-agent ratio between two values of the same sign, and we also identify limitations of our approach via impossibility results on the use of buffers with smaller size.

cs.GT↗

arXiv2025

Training Hybrid Deep Quantum Neural Network for Efficient Reinforcement Learning

Quantum circuits embed data in a Hilbert space whose dimensionality grows exponentially with the number of qubits, allowing even shallow parameterised quantum circuits (PQCs) to represent highly-correlated probability distributions that are costly for classical networks to capture. Reinforcement-learning (RL) agents, which must reason over long-horizon, continuous-control tasks, stand to benefit from this expressive quantum feature space, but only if the quantum layers can be trained jointly with the surrounding deep-neural components. Current gradient-estimation techniques (e.g., parameter-shift rule) make such hybrid training impractical for realistic RL workloads, because every gradient step requires a prohibitive number of circuit evaluations and thus erodes the potential quantum advantage. We introduce qtDNN, a tangential surrogate that locally approximates a PQC with a small differentiable network trained on-the-fly from the same minibatch. Embedding qtDNN inside the computation graph yields scalable batch gradients while keeping the original quantum layer for inference. Building on qtDNN we design hDQNN-TD3, a hybrid deep quantum neural network for continuous-control reinforcement learning based on the TD3 architecture, which matches or exceeds state-of-the-art classical performance on popular benchmarks. The method opens a path toward applying hybrid quantum models to large-scale RL and other gradient-intensive machine-learning tasks.

quant-ph↗

arXiv2025

Federated Sinkhorn

We study distributed Sinkhorn iterations for entropy-regularized optimal transport when the Gibbs kernel operator is row-partitioned across c workers and cannot be centralized. We present Federated Sinkhorn, two exact synchronous protocols that exchange only scaling-vector slices: (i) an All-to-All scheme implemented by Allgather, and (ii) a Star (parameter-server) scheme implemented by client to server sends and server to client broadcasts. For both, we derive closed-form per-iteration compute, communication, and memory costs under an alpha-beta latency--bandwidth model, and show that the distributed iterates match centralized Sinkhorn under standard positivity assumptions. Multi-node CPU/GPU experiments validate the model and show that repeated global scaling exchange quickly becomes the dominant bottleneck as c increases. We also report an optional bounded-delay asynchronous schedule and an optional privacy measurement layer for communicated log-scalings.

cs.DC↗

arXiv2020

Smart Meter Data Privacy

Smart grids (SGs) promise to deliver dramatic improvements compared to traditional power grids thanks primarily to the large amount of data being exchanged and processed within the grid, which enables the grid to be monitored more accurately and at a much faster pace. The smart meter (SM) is one of the key devices that enable the SG concept by monitoring a household's electricity consumption and reporting it to the utility provider (UP), i.e., the entity that sells energy to customers, or to the distribution system operator (DSO), i.e., the entity that operates and manages the grid, with high accuracy and at a much faster pace compared to traditional meters. However, the very availability of rich and high-frequency household electricity consumption data, which enables a very efficient power grid management, also opens up unprecedented challenges on data security and privacy. To counter these threats, it is necessary to develop techniques that keep SM data private, and, for this reason, SM privacy has become a very active research area. The aim of this chapter is to provide an overview of the most significant privacy-preserving techniques for SM data, highlighting their main benefits and disadvantages.

cs.IT↗

arXiv2020

Privacy-Cost Trade-offs in Smart Electricity Metering Systems

Trade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery is available to the consumer, which can be utilized both to achieve privacy and to reduce the energy cost by demand shaping. Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use (ToU) pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve the privacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterized. A more practical EMP, which optimizes the energy management less frequently, is also considered. Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators.

cs.IT↗

arXiv2018

Privacy-Aware Smart Metering: Progress and Challenges

The next-generation energy network, the so-called smart grid (SG), promises a tremendous increase in efficiency, safety and flexibility of managing the electricity grid as compared to the legacy energy network. This is needed today more than ever, as the global energy consumption is growing at an unprecedented rate, and renewable energy sources have to be seamlessly integrated into the grid to assure a sustainable human development. Smart meters (SMs) are among the crucial enablers of the SG concept; they supply accurate high-frequency information about users' household energy consumption to a utility provider, which is essential for time of use pricing, rapid fault detection, energy theft prevention, while also providing consumers with more flexibility and control over their consumption. However, highly accurate and granular SM data also poses a threat to consumer privacy as non-intrusive load monitoring techniques enable a malicious attacker to infer many details of a user's private life. This article focuses on privacy-enhancing energy management techniques that provide accurate energy consumption information to the grid operator, without sacrificing consumer privacy. In particular, we focus on techniques that shape and modify the actual user energy consumption by means of physical resources, such as rechargeable batteries, renewable energy sources or demand shaping. A rigorous mathematical analysis of privacy is presented under various physical constraints on the available physical resources. Finally, open questions and challenges that need to be addressed to pave the way to the effective protection of users' privacy in future SGs are presented.

cs.IT↗

arXiv2018

Smart Meter Privacy with Renewable Energy and an Energy Storage Device

A smart meter (SM) measures a consumer's electricity consumption and reports it automatically to a utility provider (UP) in almost real time. Despite many advantages of SMs, their use also leads to serious concerns about consumer privacy. In this paper, SM privacy is studied by considering the presence of a renewable energy source (RES) and a rechargeable battery (RB), which can be used to partially hide the consumer's energy consumption behavior. Privacy is measured by the information leakage rate, which denotes the average mutual information between the user's real energy consumption and the energy requested from the grid, which the SM reads and reports to the UP. The impact of the knowledge of the amount of energy generated by the RES at the UP is also considered. The minimum information leakage rate is characterized as a computable information theoretic single-letter expression in the two extreme cases, that is, when the battery capacity is infinite or zero. Numerical results are presented for the finite battery capacity case to illustrate the potential privacy gains from the existence of an RB. It is shown that, while the information leakage rate decreases with increasing availability of an RES, larger storage capacity is needed to fully exploit the available energy to improve the privacy.

cs.IT↗

arXiv2017

Optimal Demand-Side Management for Joint Privacy-Cost Optimization with Energy Storage

The smart meter (SM) privacy problem is addressed together with the cost of energy for the user. It is assumed that a storage device, e.g., an electrical battery, is available to the user, which can be utilized both to achieve privacy and to reduce the energy cost by modifying the energy consumption profile. Privacy is measured via the mean squared-error between the SM readings, which are reported to the utility provider (UP), and a target load; while time-of-use pricing is considered for energy cost calculation. The optimal trade-off between the achievable privacy and the energy cost is characterized by taking into account the limited capacity of the battery as well as the capability to sell energy to the UP. Extensive numerical simulations are presented to evaluate the performance of the proposed strategy for different system settings.

cs.IT↗

arXiv2016

Smart Meter Privacy with Renewable Energy and a Finite Capacity Battery

We address the smart meter (SM) privacy problem by considering the availability of a renewable energy source (RES) and a battery which can be exploited by a consumer to partially hide the consumption pattern from the utility provider (UP). Privacy is measured by the mutual information rate between the consumer's energy consumption and the renewable energy generation process, and the energy received from the grid, where the latter is known by the UP through the SM readings, and the former two are to be kept private. By expressing the information leakage as an additive quantity, we cast the problem as a stochastic control problem, and formulate the corresponding Bellman equations.

cs.IT↗

arXiv2015

Smart Meter Privacy with an Energy Harvesting Device and Instantaneous Power Constraints

A smart meter (SM) periodically measures end-user electricity consumption and reports it to a utility provider (UP). Despite the advantages of SMs, their use leads to serious concerns about consumer privacy. In this paper, SM privacy is studied by considering the presence of an energy harvesting device (EHD) as a means of masking the user's input load. The user can satisfy part or all of his/her energy needs from the EHD, and hence, less information can be leaked to the UP via the SM. The EHD is typically equipped with a rechargeable energy storage device, i.e., a battery, whose instantaneous energy content limits the user's capability in covering his/her energy usage. Privacy is measured by the information leaked about the user's real energy consumption when the UP observes the energy requested from the grid, which the SM reads and reports to the UP. The minimum information leakage rate is characterized as a computable information theoretic single-letter expression when the EHD battery capacity is either infinite or zero. Numerical results are presented for a discrete binary input load to illustrate the potential privacy gains from the existence of a storage device.

cs.IT↗