Publishing Asynchronous Event Times with Pufferfish Privacy

Jiaxin Ding; Abhirup Ghosh; Rik Sarkar; Jie Gao

doi:10.1109/DCOSS54816.2022.00020

Publishing Asynchronous Event Times with Pufferfish Privacy

Jiaxin Ding, Abhirup Ghosh, Rik Sarkar, Jie Gao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.

Original language	English
Title of host publication	Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	53-60
Number of pages	8
ISBN (Electronic)	978-1-6654-9512-7
ISBN (Print)	978-1-6654-9513-4
DOIs	https://doi.org/10.1109/DCOSS54816.2022.00020
Publication status	Published - 12 Sept 2022
Event	The 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022 - Los Angeles, United States Duration: 30 May 2022 → 1 Jun 2022 Conference number: 18

Publication series

Name	International Conference on Distributed Computing in Sensor Systems (DCOSS)
Publisher	IEEE
ISSN (Print)	2325-2936
ISSN (Electronic)	2325-2944

Conference

Conference	The 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022
Abbreviated title	DCOSS 2022
Country/Territory	United States
City	Los Angeles
Period	30/05/22 → 1/06/22

Access to Document

10.1109/DCOSS54816.2022.00020Licence: All Rights Reserved

Publishing_timestamps_with_statistical_privacy (1)Accepted author manuscript, 1.31 MBLicence: All Rights Reserved

https://ieeexplore.ieee.org/document/9881735Licence: All Rights Reserved

Cite this

Ding, J., Ghosh, A., Sarkar, R., & Gao, J. (2022). Publishing Asynchronous Event Times with Pufferfish Privacy. In Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022 (pp. 53-60). (International Conference on Distributed Computing in Sensor Systems (DCOSS)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/DCOSS54816.2022.00020

@inproceedings{1fee4ca38da445b9903106921dd1550a,

title = "Publishing Asynchronous Event Times with Pufferfish Privacy",

abstract = "Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.",

author = "Jiaxin Ding and Abhirup Ghosh and Rik Sarkar and Jie Gao",

note = "Funding Information: J. Ding would like to acknowledgesupports from Shanghai Sailing Program 20YF1421300, Natural Science Foundation of Shanghai No. 22ZR1429100. A. Ghosh would like to ac-knowledgesupports from WelcomeTrust (Grant No. 213939). J. Gao would like to acknowledge supports from NSF CCF-2118953 and OAC-1939459. Funding Information: J. Ding would like to acknowledge supports from Shanghai Sailing Program 20YF1421300, Natural Science Foundation of Shanghai No. 22ZR1429100. A. Ghosh would like to acknowledge supports from Welcome Trust (Grant No. 213939). J. Gao would like to acknowledge supports from NSF CCF- 2118953 and OAC-1939459. Publisher Copyright: {\textcopyright} 2022 IEEE.; The 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022, DCOSS 2022 ; Conference date: 30-05-2022 Through 01-06-2022",

year = "2022",

month = sep,

day = "12",

doi = "10.1109/DCOSS54816.2022.00020",

language = "English",

isbn = "978-1-6654-9513-4",

series = "International Conference on Distributed Computing in Sensor Systems (DCOSS)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "53--60",

booktitle = "Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022",

address = "United States",

}

Ding, J, Ghosh, A, Sarkar, R & Gao, J 2022, Publishing Asynchronous Event Times with Pufferfish Privacy. in Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022. International Conference on Distributed Computing in Sensor Systems (DCOSS), Institute of Electrical and Electronics Engineers (IEEE), pp. 53-60, The 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022, Los Angeles, California, United States, 30/05/22. https://doi.org/10.1109/DCOSS54816.2022.00020

Publishing Asynchronous Event Times with Pufferfish Privacy. / Ding, Jiaxin; Ghosh, Abhirup; Sarkar, Rik et al.
Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022. Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 53-60 (International Conference on Distributed Computing in Sensor Systems (DCOSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Publishing Asynchronous Event Times with Pufferfish Privacy

AU - Ding, Jiaxin

AU - Ghosh, Abhirup

AU - Sarkar, Rik

AU - Gao, Jie

N1 - Conference code: 18

PY - 2022/9/12

Y1 - 2022/9/12

N2 - Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.

AB - Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not; whether one can nail down the timestamp of an event within a given time interval; and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location check-in and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.

U2 - 10.1109/DCOSS54816.2022.00020

DO - 10.1109/DCOSS54816.2022.00020

M3 - Conference contribution

SN - 978-1-6654-9513-4

T3 - International Conference on Distributed Computing in Sensor Systems (DCOSS)

SP - 53

EP - 60

BT - Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - The 18th Annual International Conference on Distributed Computing in Sensor Systems, 2022

Y2 - 30 May 2022 through 1 June 2022

ER -

Ding J, Ghosh A, Sarkar R, Gao J. Publishing Asynchronous Event Times with Pufferfish Privacy. In Proceedings of The 18th International Conference on Distributed Computing in Sensor Systems 2022. Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 53-60. (International Conference on Distributed Computing in Sensor Systems (DCOSS)). doi: 10.1109/DCOSS54816.2022.00020

Publishing Asynchronous Event Times with Pufferfish Privacy

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