---
OA_place: repository
_id: '21401'
abstract:
- lang: eng
  text: "Runtime verification offers scalable solutions to improve the safety and
    reliability of systems. However, systems that require verification or monitoring
    by a third party to ensure compliance with a specification might contain sensitive
    information, causing privacy concerns when usual runtime verification approaches
    are used. Privacy is compromised if protected information about the system, or
    sensitive data that is processed by the system, is revealed. In addition, revealing
    the specification being monitored may undermine the essence of third-party verification.\r\n\r\nIn
    this thesis, we propose a protocol for privacy-preserving runtime verification
    of systems against formal sequential specifications. We develop the protocol in
    two steps. In the first step, the monitor verifies whether the system satisfies
    the specification without learning anything else, though both parties are aware
    of the specification. In the second step, we extend the protocol to ensure that
    the system remains oblivious to the monitored specification, while the monitor
    learns only whether the system satisfies the specification and nothing more. Our
    protocol adapts and improves existing techniques used in cryptography, and more
    specifically, multi-party computation.\r\n\r\nThe sequential specification defines
    the observation step of the monitor, whose granularity depends on the situation
    (e.g., banks may be monitored on a daily basis). Our protocol exchanges a single
    message per observation step, after an initialization phase. This design minimizes
    communication overhead, enabling relatively lightweight privacy-preserving monitoring.
    We implement our approach for monitoring specifications described by register
    automata and evaluate it experimentally.\r\n"
acknowledgement: "This work is part of the project VAMOS, which has received funding
  from the European\r\nResearch Council (ERC) under grant agreement No. 101020093,
  and the Austrian Science\r\nFund (FWF) SFB project SpyCoDe F8502.\r\n"
alternative_title:
- ISTA Master’s Thesis
article_processing_charge: No
author:
- first_name: Mahyar
  full_name: Karimi, Mahyar
  id: 6e5417ba-5355-11ee-ae5a-94c2e510b26b
  last_name: Karimi
  orcid: 0009-0005-0820-1696
citation:
  ama: Karimi M. Privacy-preserving runtime verification. 2026. doi:<a href="https://doi.org/10.15479/AT-ISTA-21401">10.15479/AT-ISTA-21401</a>
  apa: Karimi, M. (2026). <i>Privacy-preserving runtime verification</i>. Institute
    of Science and Technology Austria. <a href="https://doi.org/10.15479/AT-ISTA-21401">https://doi.org/10.15479/AT-ISTA-21401</a>
  chicago: Karimi, Mahyar. “Privacy-Preserving Runtime Verification.” Institute of
    Science and Technology Austria, 2026. <a href="https://doi.org/10.15479/AT-ISTA-21401">https://doi.org/10.15479/AT-ISTA-21401</a>.
  ieee: M. Karimi, “Privacy-preserving runtime verification,” Institute of Science
    and Technology Austria, 2026.
  ista: Karimi M. 2026. Privacy-preserving runtime verification. Institute of Science
    and Technology Austria.
  mla: Karimi, Mahyar. <i>Privacy-Preserving Runtime Verification</i>. Institute of
    Science and Technology Austria, 2026, doi:<a href="https://doi.org/10.15479/AT-ISTA-21401">10.15479/AT-ISTA-21401</a>.
  short: M. Karimi, Privacy-Preserving Runtime Verification, Institute of Science
    and Technology Austria, 2026.
corr_author: '1'
date_created: 2026-03-05T15:20:47Z
date_published: 2026-03-05T00:00:00Z
date_updated: 2026-03-13T13:37:20Z
day: '05'
ddc:
- '000'
degree_awarded: MS
department:
- _id: GradSch
- _id: ToHe
doi: 10.15479/AT-ISTA-21401
ec_funded: 1
file:
- access_level: open_access
  checksum: 3f49f05c9d123e14d7adb73d3bc50fe2
  content_type: application/pdf
  creator: mkarimi
  date_created: 2026-03-06T14:06:25Z
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  date_created: 2026-03-06T14:06:25Z
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  file_size: 1243394
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file_date_updated: 2026-03-10T15:20:09Z
fulldoi: https://doi.org/10.15479/AT-ISTA-21401
has_accepted_license: '1'
keyword:
- Privacy-preserving verification
- Runtime verification
- Monitoring
- Reactive functionalities
- Cryptographic protocols
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '60'
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
- _id: 34a4ce89-11ca-11ed-8bc3-8cc37fb6e11f
  grant_number: F8512
  name: Security and Privacy by Design for Complex Systems
publication_identifier:
  issn:
  - 2791-4585
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
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  - id: '21020'
    relation: part_of_dissertation
    status: public
status: public
supervisor:
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
title: Privacy-preserving runtime verification
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
_id: '21020'
abstract:
- lang: eng
  text: "Runtime verification offers scalable solutions to improve the safety and
    reliability of systems. However, systems that require verification or monitoring
    by a third party to ensure compliance with a specification might contain sensitive
    information, causing privacy concerns when usual runtime verification approaches
    are used. Privacy is compromised if protected information about the system, or
    sensitive data that is processed by the system, is revealed. In addition, revealing
    the specification being monitored may undermine the essence of third-party verification.\r\nIn
    this work, we propose two novel protocols for the privacy-preserving runtime verification
    of systems against formal sequential specifications. In our first protocol, the
    monitor verifies whether the system satisfies the specification without learning
    anything else, though both parties are aware of the specification. Our second
    protocol ensures that the system remains oblivious to the monitored specification,
    while the monitor learns only whether the system satisfies the specification and
    nothing more. Our protocols adapt and improve existing techniques used in cryptography,
    and more specifically, multi-party computation.\r\nThe sequential specification
    defines the observation step of the monitor, whose granularity depends on the
    situation (e.g., banks may be monitored on a daily basis). Our protocols exchange
    a single message per observation step, after an initialisation phase. This design
    minimises communication overhead, enabling relatively lightweight privacy-preserving
    monitoring. We implement our approach for monitoring specifications described
    by register automata and evaluate it experimentally."
acknowledgement: This work is a part of projects VAMOS that has received fund-ing
  from the European Research Council (ERC), grant agreementNo 101020093 and the Austrian
  Science Fund (FWF) SFB projectSpyCoDe F8502.We thank anonymous reviewers for pointing
  us to related work [ 3] and for their valuable suggestions that improved this paper.
article_processing_charge: Yes (via OA deal)
arxiv: 1
author:
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
- first_name: Mahyar
  full_name: Karimi, Mahyar
  id: 6e5417ba-5355-11ee-ae5a-94c2e510b26b
  last_name: Karimi
  orcid: 0009-0005-0820-1696
- first_name: K. S.
  full_name: Thejaswini, K. S.
  id: 3807fb92-fdc1-11ee-bb4a-b4d8a431c753
  last_name: Thejaswini
citation:
  ama: 'Henzinger TA, Karimi M, Thejaswini KS. Privacy-preserving runtime verification.
    In: <i>Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications
    Security</i>. Association for Computing Machinery; 2025:2774-2787. doi:<a href="https://doi.org/10.1145/3719027.3765137">10.1145/3719027.3765137</a>'
  apa: 'Henzinger, T. A., Karimi, M., &#38; Thejaswini, K. S. (2025). Privacy-preserving
    runtime verification. In <i>Proceedings of the 2025 ACM SIGSAC Conference on Computer
    and Communications Security</i> (pp. 2774–2787). Taipei, Taiwan: Association for
    Computing Machinery. <a href="https://doi.org/10.1145/3719027.3765137">https://doi.org/10.1145/3719027.3765137</a>'
  chicago: Henzinger, Thomas A, Mahyar Karimi, and K. S. Thejaswini. “Privacy-Preserving
    Runtime Verification.” In <i>Proceedings of the 2025 ACM SIGSAC Conference on
    Computer and Communications Security</i>, 2774–87. Association for Computing Machinery,
    2025. <a href="https://doi.org/10.1145/3719027.3765137">https://doi.org/10.1145/3719027.3765137</a>.
  ieee: T. A. Henzinger, M. Karimi, and K. S. Thejaswini, “Privacy-preserving runtime
    verification,” in <i>Proceedings of the 2025 ACM SIGSAC Conference on Computer
    and Communications Security</i>, Taipei, Taiwan, 2025, pp. 2774–2787.
  ista: 'Henzinger TA, Karimi M, Thejaswini KS. 2025. Privacy-preserving runtime verification.
    Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security.
    CCS: Conference on Computer and Communications Security, 2774–2787.'
  mla: Henzinger, Thomas A., et al. “Privacy-Preserving Runtime Verification.” <i>Proceedings
    of the 2025 ACM SIGSAC Conference on Computer and Communications Security</i>,
    Association for Computing Machinery, 2025, pp. 2774–87, doi:<a href="https://doi.org/10.1145/3719027.3765137">10.1145/3719027.3765137</a>.
  short: T.A. Henzinger, M. Karimi, K.S. Thejaswini, in:, Proceedings of the 2025
    ACM SIGSAC Conference on Computer and Communications Security, Association for
    Computing Machinery, 2025, pp. 2774–2787.
conference:
  end_date: 2025-10-17
  location: Taipei, Taiwan
  name: 'CCS: Conference on Computer and Communications Security'
  start_date: 2025-10-13
corr_author: '1'
date_created: 2026-01-20T10:17:10Z
date_published: 2025-11-22T00:00:00Z
date_updated: 2026-03-13T13:37:19Z
day: '22'
ddc:
- '000'
department:
- _id: ToHe
- _id: GradSch
doi: 10.1145/3719027.3765137
ec_funded: 1
external_id:
  arxiv:
  - '2505.09276'
file:
- access_level: open_access
  checksum: 615ffddab6c7285158c2953acec6fa6f
  content_type: application/pdf
  creator: dernst
  date_created: 2026-01-21T07:34:58Z
  date_updated: 2026-01-21T07:34:58Z
  file_id: '21024'
  file_name: 2025_CCS_HenzingerT.pdf
  file_size: 1241912
  relation: main_file
  success: 1
file_date_updated: 2026-01-21T07:34:58Z
fulldoi: https://doi.org/10.1145/3719027.3765137
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '11'
oa: 1
oa_version: Published Version
page: 2774-2787
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
- _id: 34a1b658-11ca-11ed-8bc3-c75229f0241e
  grant_number: F8502
  name: Interface Theory for Security and Privacy
publication: Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications
  Security
publication_identifier:
  isbn:
  - '9798400715259'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
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  - id: '21401'
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    status: public
scopus_import: '1'
status: public
title: Privacy-preserving runtime verification
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...
---
_id: '13310'
abstract:
- lang: eng
  text: Machine-learned systems are in widespread use for making decisions about humans,
    and it is important that they are fair, i.e., not biased against individuals based
    on sensitive attributes. We present runtime verification of algorithmic fairness
    for systems whose models are unknown, but are assumed to have a Markov chain structure.
    We introduce a specification language that can model many common algorithmic fairness
    properties, such as demographic parity, equal opportunity, and social burden.
    We build monitors that observe a long sequence of events as generated by a given
    system, and output, after each observation, a quantitative estimate of how fair
    or biased the system was on that run until that point in time. The estimate is
    proven to be correct modulo a variable error bound and a given confidence level,
    where the error bound gets tighter as the observed sequence gets longer. Our monitors
    are of two types, and use, respectively, frequentist and Bayesian statistical
    inference techniques. While the frequentist monitors compute estimates that are
    objectively correct with respect to the ground truth, the Bayesian monitors compute
    estimates that are correct subject to a given prior belief about the system’s
    model. Using a prototype implementation, we show how we can monitor if a bank
    is fair in giving loans to applicants from different social backgrounds, and if
    a college is fair in admitting students while maintaining a reasonable financial
    burden on the society. Although they exhibit different theoretical complexities
    in certain cases, in our experiments, both frequentist and Bayesian monitors took
    less than a millisecond to update their verdicts after each observation.
acknowledgement: 'This work is supported by the European Research Council under Grant
  No.: ERC-2020-AdG101020093.'
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
arxiv: 1
author:
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
- first_name: Mahyar
  full_name: Karimi, Mahyar
  id: 6e5417ba-5355-11ee-ae5a-94c2e510b26b
  last_name: Karimi
  orcid: 0009-0005-0820-1696
- first_name: Konstantin
  full_name: Kueffner, Konstantin
  id: 8121a2d0-dc85-11ea-9058-af578f3b4515
  last_name: Kueffner
  orcid: 0000-0001-8974-2542
- first_name: Kaushik
  full_name: Mallik, Kaushik
  id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
  last_name: Mallik
  orcid: 0000-0001-9864-7475
citation:
  ama: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Monitoring algorithmic fairness.
    In: <i>Computer Aided Verification</i>. Vol 13965. Springer Nature; 2023:358–382.
    doi:<a href="https://doi.org/10.1007/978-3-031-37703-7_17">10.1007/978-3-031-37703-7_17</a>'
  apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., &#38; Mallik, K. (2023). Monitoring
    algorithmic fairness. In <i>Computer Aided Verification</i> (Vol. 13965, pp. 358–382).
    Paris, France: Springer Nature. <a href="https://doi.org/10.1007/978-3-031-37703-7_17">https://doi.org/10.1007/978-3-031-37703-7_17</a>'
  chicago: Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik.
    “Monitoring Algorithmic Fairness.” In <i>Computer Aided Verification</i>, 13965:358–382.
    Springer Nature, 2023. <a href="https://doi.org/10.1007/978-3-031-37703-7_17">https://doi.org/10.1007/978-3-031-37703-7_17</a>.
  ieee: T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Monitoring algorithmic
    fairness,” in <i>Computer Aided Verification</i>, Paris, France, 2023, vol. 13965,
    pp. 358–382.
  ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Monitoring algorithmic
    fairness. Computer Aided Verification. CAV: Computer Aided Verification, LNCS,
    vol. 13965, 358–382.'
  mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness.” <i>Computer
    Aided Verification</i>, vol. 13965, Springer Nature, 2023, pp. 358–382, doi:<a
    href="https://doi.org/10.1007/978-3-031-37703-7_17">10.1007/978-3-031-37703-7_17</a>.
  short: T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, Computer Aided Verification,
    Springer Nature, 2023, pp. 358–382.
conference:
  end_date: 2023-07-22
  location: Paris, France
  name: 'CAV: Computer Aided Verification'
  start_date: 2023-07-17
corr_author: '1'
date_created: 2023-07-25T18:32:40Z
date_published: 2023-07-18T00:00:00Z
date_updated: 2026-09-18T07:41:29Z
day: '18'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1007/978-3-031-37703-7_17
ec_funded: 1
external_id:
  arxiv:
  - '2305.15979'
  isi:
  - '001310804800017'
file:
- access_level: open_access
  checksum: ccaf94bf7d658ba012c016e11869b54c
  content_type: application/pdf
  creator: dernst
  date_created: 2023-07-31T08:11:20Z
  date_updated: 2023-07-31T08:11:20Z
  file_id: '13327'
  file_name: 2023_LNCS_CAV_HenzingerT.pdf
  file_size: 647760
  relation: main_file
  success: 1
file_date_updated: 2023-07-31T08:11:20Z
fulldoi: https://doi.org/10.1007/978-3-031-37703-7_17
has_accepted_license: '1'
intvolume: '     13965'
isi: 1
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 358–382
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
publication: Computer Aided Verification
publication_identifier:
  eisbn:
  - '9783031377037'
  eissn:
  - 1611-3349
  isbn:
  - '9783031377020'
  issn:
  - 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
  record:
  - id: '22808'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: Monitoring algorithmic fairness
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13965
year: '2023'
...
---
_id: '13228'
abstract:
- lang: eng
  text: A machine-learned system that is fair in static decision-making tasks may
    have biased societal impacts in the long-run. This may happen when the system
    interacts with humans and feedback patterns emerge, reinforcing old biases in
    the system and creating new biases. While existing works try to identify and mitigate
    long-run biases through smart system design, we introduce techniques for monitoring
    fairness in real time. Our goal is to build and deploy a monitor that will continuously
    observe a long sequence of events generated by the system in the wild, and will
    output, with each event, a verdict on how fair the system is at the current point
    in time. The advantages of monitoring are two-fold. Firstly, fairness is evaluated
    at run-time, which is important because unfair behaviors may not be eliminated
    a priori, at design-time, due to partial knowledge about the system and the environment,
    as well as uncertainties and dynamic changes in the system and the environment,
    such as the unpredictability of human behavior. Secondly, monitors are by design
    oblivious to how the monitored system is constructed, which makes them suitable
    to be used as trusted third-party fairness watchdogs. They function as computationally
    lightweight statistical estimators, and their correctness proofs rely on the rigorous
    analysis of the stochastic process that models the assumptions about the underlying
    dynamics of the system. We show, both in theory and experiments, how monitors
    can warn us (1) if a bank’s credit policy over time has created an unfair distribution
    of credit scores among the population, and (2) if a resource allocator’s allocation
    policy over time has made unfair allocations. Our experiments demonstrate that
    the monitors introduce very low overhead. We believe that runtime monitoring is
    an important and mathematically rigorous new addition to the fairness toolbox.
acknowledgement: 'The authors would like to thank the anonymous reviewers for their
  valuable comments and helpful suggestions. This work is supported by the European
  Research Council under Grant No.: ERC-2020-AdG 101020093.'
article_processing_charge: Yes (via OA deal)
arxiv: 1
author:
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
- first_name: Mahyar
  full_name: Karimi, Mahyar
  id: 6e5417ba-5355-11ee-ae5a-94c2e510b26b
  last_name: Karimi
  orcid: 0009-0005-0820-1696
- first_name: Konstantin
  full_name: Kueffner, Konstantin
  id: 8121a2d0-dc85-11ea-9058-af578f3b4515
  last_name: Kueffner
  orcid: 0000-0001-8974-2542
- first_name: Kaushik
  full_name: Mallik, Kaushik
  id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
  last_name: Mallik
  orcid: 0000-0001-9864-7475
citation:
  ama: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Runtime monitoring of dynamic
    fairness properties. In: <i>FAccT ’23: Proceedings of the 2023 ACM Conference
    on Fairness, Accountability, and Transparency</i>. Association for Computing Machinery;
    2023:604-614. doi:<a href="https://doi.org/10.1145/3593013.3594028">10.1145/3593013.3594028</a>'
  apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., &#38; Mallik, K. (2023). Runtime
    monitoring of dynamic fairness properties. In <i>FAccT ’23: Proceedings of the
    2023 ACM Conference on Fairness, Accountability, and Transparency</i> (pp. 604–614).
    Chicago, IL, United States: Association for Computing Machinery. <a href="https://doi.org/10.1145/3593013.3594028">https://doi.org/10.1145/3593013.3594028</a>'
  chicago: 'Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik.
    “Runtime Monitoring of Dynamic Fairness Properties.” In <i>FAccT ’23: Proceedings
    of the 2023 ACM Conference on Fairness, Accountability, and Transparency</i>,
    604–14. Association for Computing Machinery, 2023. <a href="https://doi.org/10.1145/3593013.3594028">https://doi.org/10.1145/3593013.3594028</a>.'
  ieee: 'T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Runtime monitoring
    of dynamic fairness properties,” in <i>FAccT ’23: Proceedings of the 2023 ACM
    Conference on Fairness, Accountability, and Transparency</i>, Chicago, IL, United
    States, 2023, pp. 604–614.'
  ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Runtime monitoring of
    dynamic fairness properties. FAccT ’23: Proceedings of the 2023 ACM Conference
    on Fairness, Accountability, and Transparency. FAccT: Conference on Fairness,
    Accountability and Transparency, 604–614.'
  mla: 'Henzinger, Thomas A., et al. “Runtime Monitoring of Dynamic Fairness Properties.”
    <i>FAccT ’23: Proceedings of the 2023 ACM Conference on Fairness, Accountability,
    and Transparency</i>, Association for Computing Machinery, 2023, pp. 604–14, doi:<a
    href="https://doi.org/10.1145/3593013.3594028">10.1145/3593013.3594028</a>.'
  short: 'T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, FAccT ’23: Proceedings
    of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Association
    for Computing Machinery, 2023, pp. 604–614.'
conference:
  end_date: 2023-06-15
  location: Chicago, IL, United States
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title: Runtime monitoring of dynamic fairness properties
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...