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Elisa Bertino

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12 papers

2 author rows

TIST Journal 2026 Journal Article

Towards Evolutionary Differential Privacy in Cross-Platform Spatial Crowdsourcing

Yong-Feng Ge
Hua Wang
Elisa Bertino
Jinli Cao
Yanchun Zhang
Zhonglong Zheng

The development of mobile web services has brought significant attention to spatial crowdsourcing. The uneven distribution of tasks and workers has led to recent research on Cross-Platform Spatial Crowdsourcing (CPSC), aiming for a multi-win situation for platforms, workers and task requesters. Previous studies on CPSC problems focused on task assignment and worker selection performance, overlooking the importance of privacy preservation. This paper addresses the existing challenges of privacy preservation and service quality by formulating a Privacy-Preserving Cross-Platform Spatial Crowdsourcing (PP-CPSC) problem and proves it to be NP-hard. We propose an Evolutionary Differential Privacy (Evo-DP) approach to optimize PP-CPSC. Evo-DP's evolutionary framework enables efficient and flexible optimization of privacy budget allocation. Within Evo-DP, each solution to the privacy budget allocation is represented as an individual in the population. To approximate the optimal solution, three evolutionary operations - mutation, crossover, and scaling - are employed for population updates, along with a selection process. A hybrid population model is introduced to balance exploration and exploitation abilities. Experimental results demonstrate Evo-DP's superiority over previous strategies in terms of solution quality, convergence speed, and scalability.

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NeurIPS Conference 2025 Conference Paper

PSMBench: A Benchmark and Dataset for Evaluating LLMs Extraction of Protocol State Machines from RFC Specifications

Zilin Shen
Xinyu Luo
Imtiaz Karim
Elisa Bertino

Accurately extracting protocol-state machines (PSMs) from the long, densely written Request-for-Comments (RFC) standards that govern Internet‐scale communication remains a bottleneck for automated security analysis and protocol testing. In this paper, we introduce RFC2PSM, the first large-scale dataset that pairs 1, 580 pages of cleaned RFC text with 108 manually validated states and 297 transitions covering 14 widely deployed protocols spanning the data-link, transport, session, and application layers. Built on this corpus, we propose PsmBench, a benchmark that (i) feeds chunked RFC to an LLM, (ii) prompts the model to emit a machine-readable PSM, and (iii) scores the output with structure-aware, semantic fuzzy-matching metrics that reward partially correct graphs. A comprehensive baseline study of nine state-of-the-art open and commercial LLMs reveals a persistent state–transition gap: models identify many individual states (up to $0. 82$ F1) but struggle to assemble coherent transition graphs ($\leq 0. 38$ F1), highlighting challenges in long-context reasoning, alias resolution, and action/event disambiguation. We release the dataset, evaluation code, and all model outputs as open-sourced, providing a fully reproducible starting point for future work on reasoning over technical prose and generating executable graph structures. RFC2PSM and PsmBench aim to catalyze cross-disciplinary progress toward LLMs that can interpret and verify the protocols that keep the Internet safe.

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IS Journal 2022 Journal Article

CryptoCliqIn: Graph-Theoretic Cryptography Using Clique Injection

Srinibas Swain
Deepak Puthal
Elisa Bertino

Because encryption is a fundamental security building block, existing encryption techniques like AES, Twofish, Blowfish, and Triple DES are constantly under the threat of being compromised. We introduce a simple graph-theoretic encryption method named CryptoCliqIn using clique injection and prove that the decryption of this encryption without the appropriate key is #P-complete. We have shown that the proposed model does not introduce delays in encryption and decryption times and provides a more secure mechanism compared to some of the existing encryption mechanisms. Finally, an adaptation of CryptoCliqIn in an intelligent system is discussed under the setup of intelligent and smart building.

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IJCAI Conference 2021 Conference Paper

Scalable Non-observational Predicate Learning in ASP

Mark Law
Alessandra Russo
Krysia Broda
Elisa Bertino

Recently, novel ILP systems under the answer set semantics have been proposed, some of which are robust to noise and scalable over large hypothesis spaces. One such system is FastLAS, which is significantly faster than other state-of-the-art ASP-based ILP systems. FastLAS is, however, only capable of Observational Predicate Learning (OPL), where the learned hypothesis defines predicates that are directly observed in the examples. It cannot learn knowledge that is indirectly observable, such as learning causes of observed events. This class of problems, known as non-OPL, is known to be difficult to handle in the context of non-monotonic semantics. Solving non-OPL learning tasks whilst preserving scalability is a challenging open problem. We address this problem with a new abductive method for translating examples of a non-OPL task to a set of examples, called possibilities, such that the original example is covered iff at least one of the possibilities is covered. This new method allows an ILP system capable of performing OPL tasks to be "upgraded" to solve non-OPL tasks. In particular, we present our new FastNonOPL system, which upgrades FastLAS with the new possibility generation. We compare it to other state-of-the-art ASP-based ILP systems capable of solving non-OPL tasks, showing that FastNonOPL is significantly faster, and in many cases more accurate, than these other systems.

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AAAI Conference 2020 Conference Paper

FastLAS: Scalable Inductive Logic Programming Incorporating Domain-Specific Optimisation Criteria

Mark Law
Alessandra Russo
Elisa Bertino
Krysia Broda
Jorge Lobo

Inductive Logic Programming (ILP) systems aim to ﬁnd a set of logical rules, called a hypothesis, that explain a set of examples. In cases where many such hypotheses exist, ILP systems often bias towards shorter solutions, leading to highly general rules being learned. In some application domains like security and access control policies, this bias may not be desirable, as when data is sparse more speciﬁc rules that guarantee tighter security should be preferred. This paper presents a new general notion of a scoring function over hypotheses that allows a user to express domain-speciﬁc optimisation criteria. This is incorporated into a new ILP system, called FastLAS, that takes as input a learning task and a customised scoring function, and computes an optimal solution with respect to the given scoring function. We evaluate the accuracy of Fast- LAS over real-world datasets for access control policies and show that varying the scoring function allows a user to target domain-speciﬁc performance metrics. We also compare FastLAS to state-of-the-art ILP systems, using the standard ILP bias for shorter solutions, and demonstrate that FastLAS is signiﬁcantly faster and more scalable.

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AAAI Conference 2019 Conference Paper

Representing and Learning Grammars in Answer Set Programming

Mark Law
Alessandra Russo
Elisa Bertino
Krysia Broda
Jorge Lobo

In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed.

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TIME Conference 2002 Conference Paper

Evolution Specification of Multigranular Temporal Objects

Elena Camossi
Elisa Bertino
Giovanna Guerrini
Marco Mesiti

The main key feature of temporal databases is to maintain all values taken by object attributes over time. Since historical information may be needed at different levels of detail, multigranular temporal databases have been introduced, in which different attributes can be stored at different temporal granularities. An important issue that has not been addressed, however is that the required level of detail does not only depend on attribute semantics, rather it is often related to how recent the data are. It is quite natural that recent data are needed at greater level of detail, whereas less detail is needed as data age. As an extreme case, data can also expire, that is, they are no longer needed after a certain period of time. In this paper we address the problem of evolution and expiration of historical data in a multigranular temporal object model.

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TIME Conference 2001 Conference Paper

A Linguistic Framework for Querying Dimensional Data

Elisa Bertino
Tsz S. Cheng
Shashi K. Gadia
Giovanna Guerrini

This paper deals with dimensional data. Examples of dimensions are space and time. Thus, temporal, spatial, spatiotemporal values are examples of dimensional data. We define the notion of dimensional object, extending an object-oriented ODMG-like type system to include dimensional types. We then address the problem of querying dimensional objects. Linguistic constructs are introduced that allow objects with different dimensions to be mixed in the same phrases. This allows the user to formulate both associative and navigational accesses seamlessly without having to worry about the dimensions of the various data elements involved.

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TIME Conference 2001 Conference Paper

Navigating Through Multiple Temporal Granularity Objects

Elisa Bertino
Elena Ferrari 0001
Giovanna Guerrini
Isabella Merlo

Managing and relating temporal information at different time units is an important issue in many applications and research areas, among them temporal object-oriented databases. Due to the semantic richness of the object-oriented data model, the introduction of multiple temporal granularities in such a model poses several interesting issues. In particular, object-oriented query languages provide a navigational approach to data access, performed via path expressions. We present an extension to path expressions to a multi-granularity context. The syntax and semantics of the proposed path expressions are formally defined.

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TIME Conference 2000 Conference Paper

Querying Multiple Temporal Granularity Data

Isabella Merlo
Elisa Bertino
Elena Ferrari 0001
Shashi K. Gadia
Giovanna Guerrini

Managing and querying information with varying temporal granularities is an important problem in databases. Although there is a substantial body of work on temporal granularities for the relational data model (Snodgrass, 1995), a comprehensive framework is lacking for the object-oriented paradigm. To the best of our knowledge, a formal treatment of temporal queries with multiple granularities has not been considered in the literature. We make a step in this direction. We formally introduce the syntax and semantics of expressions involving data with multiple granularities, comparison between data with different granularities, and conversion of data from one granularity to another. We believe that this is an important step towards the development of an object-oriented query language that supports multiple granularities.

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TIME Conference 1999 Conference Paper

A Temporal Object-Oriented Data Model with Multiple Granularities

Isabella Merlo
Elisa Bertino
Elena Ferrari 0001
Giovanna Guerrini

We investigate some issues arising from the introduction of multiple temporal granularities in an object-oriented data model. Although issues concerning temporal granularities have been investigated in the context of temporal relational database systems, no comparable amount of work has been done in the context of object-oriented models. Moreover, the main drawback of the existing proposals is the lack of a formal basis-which we believe is essential to manage the inherent complexity of the object-oriented data model. We provide a complete temporal object-oriented type system supporting multiple temporal granularities and we formally define the set of legal values for our type system. We then address issues related to inheritance, type refinement and substitutability.

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TIME Conference 1998 Conference Paper

An Approach to Model and Query Event-Based Temporal Data

Elisa Bertino
Elena Ferrari 0001
Giovanna Guerrini

Temporal database systems support all functions related to the management of large amounts of constantly changing data. However, current temporal database systems support a flat view of the history of data changes, in that all the changes are considered equally relevant and are, therefore, all stored in the database. However, many applications, such as monitoring and planning applications, call for more flexibility. Monitoring applications, in particular, may require that the history of a data item is stored only whenever a certain event occurs. For other applications, the history of data changes may be less important than the event causing the changes. In this paper, we propose an event-based temporal object model which allows us to keep track of selected values within the history of a data object attribute. The portions within the history of a data object which are actually stored in the database are identified by relating them to events. In the paper, besides defining the data model, we investigate the problem of querying a database with incomplete temporal information.

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