The DOI matches its Crossref record.
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Abstract
For various reasons individuals in a sample survey may prefer not to confide to the interviewer the correct answers to certain questions. In such cases the individuals may elect not to reply at all or to reply with incorrect answers. The resulting evasive answer bias is ordinarily difficult to assess. In this paper it is argued that such bias is potentially removable through allowing the interviewee to maintain privacy through the device of randomizing his response. A randomized response method for estimating a population proportion is presented as an example. Unbiased maximum likelihood estimates are obtained and their mean square errors are compared with the mean square errors of conventional estimates under various assumptions about the underlying population.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 5
- Cochran W. G., 1963, Sampling Techniques, Second Edition
- Deming W. E., 1950, Some Theory of Sampling
- Hansen M. H., 1953, Sample Survey Methods and Theory, Volume I
- Hansen M. H., 1953, Sample Survey Methods and Theory, Volume II
- Stephan F. F., 1963, Sampling Opinions
Cited by
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
“This model is referred to as the local model of differential privacy (LDP) [406, 229].”
MethodThe Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
L -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
Sensitive questions in surveys.
Tourangeau, R., & Yan, T. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.5.859 · Cited by 3,057
Determinants of social desirability bias in sensitive surveys: a literature review
Krumpal, I. (2013) · Quality & Quantity · doi:10.1007/s11135-011-9640-9 · Cited by 2,860
Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling
John, L. K., Loewenstein, G., & Prelec, D. (2012) · Psychological Science · doi:10.1177/0956797611430953 · Cited by 2,239
Methods of coping with social desirability bias: A review
Nederhof, A. J. (1985) · European Journal of Social Psychology · doi:10.1002/ejsp.2420150303 · Cited by 2,340
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
Privacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A. (2011) · SIAM Journal on Computing · doi:10.1137/090756090 · Cited by 898
Statistical Analysis of List Experiments
Blair, G., & Imai, K. (2012) · Political Analysis · doi:10.1093/pan/mpr048 · Cited by 639
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548
Differential Privacy for Deep and Federated Learning: A Survey
Ouadrhiri, A. E., & Abdelhadi, A. (2022) · IEEE Access · doi:10.1109/access.2022.3151670 · Cited by 445
Privacy-Preserving Machine Learning: Threats and Solutions
Al-Rubaie, M., & Chang, J. M. (2019) · IEEE Security & Privacy · doi:10.1109/msec.2018.2888775 · Cited by 444
The composition theorem for differential privacy
Kairouz, P., Oh, S., & Viswanath, P. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2685505 · Cited by 408
Distributed Differential Privacy via Shuffling
Cheu, A., Smith, A., Ullman, J., Zeber, D., & Zhilyaev, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-17653-2_13 · Cited by 268
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A. (2019) · Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms · doi:10.1137/1.9781611975482.151 · Cited by 264
Trustworthy ai: A computational perspective
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261
CrypTen: Secure Multi-Party Computation Meets Machine Learning
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., & van der Maaten, L. (2021) · arXiv · doi:10.48550/arxiv.2109.00984 · Cited by 257
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., & Thakurta, A. G. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14649 · Cited by 166
An economic analysis of privacy protection and statistical accuracy as social choices
Abowd, J. M., & Schmutte, I. M. (2019) · American Economic Review · doi:10.1257/aer.20170627 · Cited by 162
Optimal schemes for discrete distribution estimation under locally differential privacy
Ye, M., & Barg, A. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2018.2809790 · Cited by 158
Context-Aware Generative Adversarial Privacy
Huang, C., Kairouz, P., Chen, X., Sankar, L., & Rajagopal, R. (2017) · Entropy · doi:10.3390/e19120656 · Cited by 156
The distributed discrete gaussian mechanism for federated learning with secure aggregation
Kairouz, P., Liu, Z., & Steinke, T. (2021) · arXiv · doi:10.48550/arxiv.2102.06387 · Cited by 117
A Review of Privacy-Preserving Federated Learning for the Internet-of-Things
Briggs, C., Fan, Z., & Andras, P. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_2 · Cited by 101
Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning
Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96
Measuring Self-Reported Wrongful Convictions Among Prisoners
Loeffler, C. E., Hyatt, J., & Ridgeway, G. (2019) · Journal of Quantitative Criminology · doi:10.1007/s10940-018-9381-1 · Cited by 95
An efficient data aggregation scheme with local differential privacy in smart grid
Gai, N., Xue, K., Zhu, B., Yang, J., Liu, J., & He, D. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.01.004 · Cited by 78
Figà-Talamanca, I., Sinnathuray, T. A., Yusof, K., Fong, C. K., Palan, V. T., Adeeb, N., Nylander, P., Onifade, A., Akin, A., Bertan, M., et al. (1986) · International Journal of Health Services · doi:10.2190/bj4f-9kjn-mfal-6x22 · Cited by 70
Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy
Salamatian, S., Zhang, A., du Pin Calmon, F., Bhamidipati, S., Fawaz, N., Kveton, B., Oliveira, P., & Taft, N. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2015.2442227 · Cited by 66
Differentially private knowledge transfer for federated learning
Qi, T., Wu, F., Wu, C., He, L., Huang, Y., & Xie, X. (2023) · Nature Communications · doi:10.1038/s41467-023-38794-x · Cited by 65
Protecting customer privacy when marketing with second-party data
Schneider, M. J., Jagpal, S., Gupta, S., Li, S., & Yu, Y. (2017) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2017.02.003 · Cited by 53
Practical and private (deep) learning without sampling or shuffling
Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., & Xu, Z. (2021) · arXiv · doi:10.48550/arxiv.2103.00039 · Cited by 52
A Summary of Survey Methodology Best Practices for Security and Privacy Researchers
Redmiles, E. M., Acar, Y., Fahl, S., & Mazurek, M. L. (2017) · Digital Repository at the University of Maryland · doi:10.13016/m22k2w · Cited by 50
Lyu, L., He, X., & Li, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.213 · Cited by 51
Sex, lies, and measurement: Consistency tests for indirect response survey methods
Chuang, E., Dupas, P., Huillery, E., & Seban, J. (2021) · Journal of Development Economics · doi:10.1016/j.jdeveco.2020.102582 · Cited by 46
Gibson, M. A., Gurmu, E., Cobo, B., Rueda, M. M., & Scott, I. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193985 · Cited by 40
Horizontal Federated Recommender System: A Survey
Wang, L., Zhou, H., Bao, Y., Yan, X., Shen, G., & Kong, X. (2024) · ACM Computing Surveys · doi:10.1145/3656165 · Cited by 37
Mayer, M. M., Bell, R., & Buchner, A. (2021) · PLOS ONE · doi:10.1371/journal.pone.0261673 · Cited by 36
Clark, S., Martin, F., McGowan, R. T. S., Smidt, J., Anderson, R., Wang, L., Turpin, T., Langenfeld-McCoy, N., Bauer, B., & Mohabbat, A. B. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2020.04.037 · Cited by 32
Secure Federated Submodel Learning
Niu, C., Wu, F., Tang, S., Hua, L., Jia, R., Lv, C., Wu, Z., & Chen, G. (2019) · arXiv · doi:10.48550/arxiv.1911.02254 · Cited by 28
ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations
Müllner, P., Lex, E., Schedl, M., & Kowald, D. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3608481 · Cited by 27
AirGapAgent: Protecting Privacy-Conscious Conversational Agents
Bagdasarian, E., Yi, R., Ghalebikesabi, S., Kairouz, P., Gruteser, M., Oh, S., Balle, B., & Ramage, D. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3658644.3690350 · Cited by 28
“Other methods that protect data, like local differential privacy (DP) [23, 24, 33, 61], are not context-aware and therefore reduce the agent’s utility by always introducing noise on user data [17, 22, 32].”
On the Power of Multiple Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26
Differential Privacy Defenses and Sampling Attacks for Membership Inference
Rahimian, S., Orekondy, T., & Fritz, M. (2021) · Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3474369.3486876 · Cited by 24
Bursztyn, L., Haaland, I., Röver, N., & Roth, C. (2025) · doi:10.3386/w33920 · Cited by 18
Local data debiasing for fairness based on generative adversarial training.
Aïvodji, U., Bidet, F., Gambs, S., Ngueveu, R. C., & Tapp, A. (2021) · Algorithms · doi:10.3390/a14030087 · Cited by 16
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
Girgis, A. M., Data, D., Diggavi, S., Kairouz, P., & Suresh, A. T. (2020) · arXiv · doi:10.48550/arxiv.2008.07180 · Cited by 16
The power of the hybrid model for mean estimation
Avent, B., Dubey, Y., & Korolova, A. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0062 · Cited by 9
Metadata
- Authors
- Warner, S. L.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1965-03
- Volume
- 60
- Issue
- 309
- Pages
- 63-69
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2013823004
- Cited by
- 3,255
- References
- 0
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.