This paper proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connectivity or sudden user departures. For increasing system reliability, data sources are organized in multiple sets, called rings, which independently work on the aggregation process. Two different protocol schemes are proposed and their failure probability, i.e., the probability that the data mining output cannot guarantee the desired level of accuracy, is analytically modeled. The privacy degree, the communication cost and the computational complexity that the schemes exhibit are also characterized. Finally, the new protocols are applied to some specific use cases, demonstrating their feasibility and attractiveness.

Rings for Privacy: an Architecture for Large Scale Privacy-Preserving Data Mining / Merani, M. L.; Croce, D.; Tinnirello, I.. - In: IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. - ISSN 1045-9219. - 32:6(2021), pp. 1340-1352. [10.1109/TPDS.2021.3049286]

Rings for Privacy: an Architecture for Large Scale Privacy-Preserving Data Mining

Merani M. L.;
2021

Abstract

This paper proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connectivity or sudden user departures. For increasing system reliability, data sources are organized in multiple sets, called rings, which independently work on the aggregation process. Two different protocol schemes are proposed and their failure probability, i.e., the probability that the data mining output cannot guarantee the desired level of accuracy, is analytically modeled. The privacy degree, the communication cost and the computational complexity that the schemes exhibit are also characterized. Finally, the new protocols are applied to some specific use cases, demonstrating their feasibility and attractiveness.
2021
dic-2020
32
6
1340
1352
Rings for Privacy: an Architecture for Large Scale Privacy-Preserving Data Mining / Merani, M. L.; Croce, D.; Tinnirello, I.. - In: IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. - ISSN 1045-9219. - 32:6(2021), pp. 1340-1352. [10.1109/TPDS.2021.3049286]
Merani, M. L.; Croce, D.; Tinnirello, I.
File in questo prodotto:
File Dimensione Formato  
TDPS_rev2_1102.pdf

Accesso riservato

Descrizione: Articolo principale
Tipologia: Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione 399.24 kB
Formato Adobe PDF
399.24 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1245944
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 5
social impact