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Probabilistic Counters for Privacy Preserving Data Aggregation.

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Title: Probabilistic Counters for Privacy Preserving Data Aggregation.
Authors: Bojko, Dominik1, Marek Klonowski, Krzysztof Grining2
Source: Discrete Mathematics & Theoretical Computer Science (DMTCS). 2026, Vol. 28 Issue 2, p1-40. 40p.
Subjects: Data protection, Aggregation (Statistics), Data structures, Social surveys, Computer security, Laplace distribution
Abstract: Probabilistic counters are well-known tools often used for space-efficient set cardinality estimation. In this paper, we investigate probabilistic counters from the perspective of preserving privacy. We use the standard, rigid differential privacy notion. The intuition is that the probabilistic counters do not reveal too much information about individuals but provide only general information about the population. Therefore, they can be used safely without violating the privacy of individuals. However, it turned out, that providing a precise, formal analysis of the privacy parameters of probabilistic counters is surprisingly difficult and needs advanced techniques and a very careful approach. We demonstrate that probabilistic counters can be used as a privacy protection mechanism without extra randomisation. That is, the inherent randomisation of the protocol is sufficient to protect privacy, even if the probabilistic counter is used multiple times. In particular, we present a specific privacy-preserving data aggregation protocol based on Morris Counter and MaxGeo Counter. Some of the results presented are devoted to counters that have not been investigated so far from the perspective of privacy protection. Another part is an improvement of the previous results. We show how our results can be used to perform distributed surveys and compare the properties of counter-based solutions and a standard Laplace method. [ABSTRACT FROM AUTHOR]
Copyright of Discrete Mathematics & Theoretical Computer Science (DMTCS) is the property of Discrete Mathematics & Theoretical Computer Science DMTCS and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Probabilistic Counters for Privacy Preserving Data Aggregation.
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  Data: <searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink><br /><searchLink fieldCode="DE" term="%22Aggregation+%28Statistics%29%22">Aggregation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Social+surveys%22">Social surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+security%22">Computer security</searchLink><br /><searchLink fieldCode="DE" term="%22Laplace+distribution%22">Laplace distribution</searchLink>
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  Data: Probabilistic counters are well-known tools often used for space-efficient set cardinality estimation. In this paper, we investigate probabilistic counters from the perspective of preserving privacy. We use the standard, rigid differential privacy notion. The intuition is that the probabilistic counters do not reveal too much information about individuals but provide only general information about the population. Therefore, they can be used safely without violating the privacy of individuals. However, it turned out, that providing a precise, formal analysis of the privacy parameters of probabilistic counters is surprisingly difficult and needs advanced techniques and a very careful approach. We demonstrate that probabilistic counters can be used as a privacy protection mechanism without extra randomisation. That is, the inherent randomisation of the protocol is sufficient to protect privacy, even if the probabilistic counter is used multiple times. In particular, we present a specific privacy-preserving data aggregation protocol based on Morris Counter and MaxGeo Counter. Some of the results presented are devoted to counters that have not been investigated so far from the perspective of privacy protection. Another part is an improvement of the previous results. We show how our results can be used to perform distributed surveys and compare the properties of counter-based solutions and a standard Laplace method. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Discrete Mathematics & Theoretical Computer Science (DMTCS) is the property of Discrete Mathematics & Theoretical Computer Science DMTCS and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Type: general
      – SubjectFull: Aggregation (Statistics)
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      – SubjectFull: Data structures
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              Text: 2026
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