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Prochlo: Strong Privacy for Analytics in the Crowd
Open accessAbstract
The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring.
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“Hamer et al. [219] proposed a communication-efficient federated algorithm for learning mixture weights on an ensemble of pre-trained models, based on communicating only a subset of the models to any one device;”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [219]
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Background
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Items 201–300 of 368
Approximate Distance-Comparison-Preserving Symmetric Encryption
Fuchsbauer, G., Ghosal, R., Hauke, N., & O’Neill, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14791-3_6 · Cited by 4
Local Differentially Private Fuzzy Counting in Stream Data Using Probabilistic Data Structures
Vatsalan, D., Bhaskar, R., & Kâafar, M. A. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3198478 · Cited by 5
“The two state-of-the-art methods based on probabilistic methods for approximate aggregation are: (1) RAPPOR proposed by Google for anonymously collecting statistics about end-user software [1] and (2) count-mean sketch (CMS) proposed by Apple for identifying frequently used words or emojis by users without compromisin…”
MethodInfluentialEPS2: Privacy Preserving Set-Valued Data Analysis in the Shuffle Model
Wang, L., Ye, Q., Hu, H., & Meng, X. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3341171 · Cited by 5
Differentially Private Ad Conversion Measurement
Delaney, J., Ghazi, B., Harrison, C., Ilvento, C., Kumar, R. D., Manurangsi, P., Pál, M., Prabhakar, K., & Raykova, M. (2024) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2024-0044 · Cited by 5
Godavarthi, S., & Dr. Venkateswara Rao, G. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.538 · Cited by 6
Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms
Ding, Z., Wang, Y., Xiao, Y., Wang, G., Zhang, D., & Kifer, D. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00728-2 · Cited by 5
“Current and planned major applications include data gathering by Google [21,7], Apple [43], and Microsoft [13]; database querying by Uber [28]; and publication of population statistics at the U.”
BackgroundSpreading the Privacy Blanket:
Gordon, D., Katz, J. I., Liang, M., & Xu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09234-3_25 · Cited by 5
“The shuffle model [7, 13, 30, 3] considers n users U1, .”
BackgroundA Study of Blockchain-Based Federated Learning
Rostami, S. M., Samet, S., & Kobti, Z. (2022) · Adaptation, learning, and optimization · doi:10.1007/978-3-031-11748-0_7 · Cited by 5
SigML: Supervised Log Anomaly with Fully Homomorphic Encryption
Trivedi, D., Boudguiga, A., & Triandopoulos, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34671-2_26 · Cited by 5
Privacy risk analysis and metrics in capturing and storing network traffic
Mastroianni, M., Palmieri, F., Ficco, M., Kozik, R., & Choraś, M. (2023) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs59211.2023.00097 · Cited by 5
Arboretum: A Planner for Large-Scale Federated Analytics with Differential Privacy
Margolin, E., Newatia, K., Luo, T., Roth, E., & Haeberlen, A. (2023) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3600006.3624566 · Cited by 5
“For instance, the shuffle model (as, e.g., in PROCHLO [13]) requires two entities, the shuffler and the analyzer, who must not collude; the anytrust model (as, e.g., in UnLynx [34] and Prio [25]) requires a group of entities that must contain at least one honest entity; and TEE-based solutions,…”
BackgroundMethodElephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget
Jin, J., Chuengsatiansup, C., Murray, T., Rubinstein, B. I. P., Yarom, Y., & Ohrimenko, O. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670281 · Cited by 5
Some commonly held but shaky assumptions about data, privacy and power
Veale, M. (2025) · Edward Elgar Publishing eBooks · doi:10.4337/9781802202335.00009 · Cited by 5
Tight Accounting in the Shuffle Model of Differential PrivacyPreprint
Koskela, A., Heikkilä, M., & Honkela, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00477 · Cited by 5
Rannenberg, K., Pape, S., Tronnier, F., & Löbner, S. (2021) · doi:10.21248/gups.63413 · Cited by 5
A systematic review on privacy preservation in federated learning
Sum, A. S. I., Pritee, Z. T., Saha, A. K., Mahmud, M. A., & Nur, K. M. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01229-x · Cited by 6
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation Under Differential Privacy
Murakami, T., Sei, Y., & Eriguchi, R. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00019 · Cited by 4
A Theory of Composition for Differential Obliviousness
Zhou, M., Shi, E., Chan, T.-H. H., & Maimon, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30620-4_1 · Cited by 4
DPrio: Efficient Differential Privacy with High Utility for Prio
Keeler, D., Komlo, C., Lepert, E., Veitch, S., & He, X. G. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0086 · Cited by 6
“This technique is commonly considered in the literature as the shuffle model [9, 26].”
MethodDistributed Linear Bandits With Differential Privacy
Li, F., Zhou, X., & Ji, B. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3362978 · Cited by 5
A Range Query Scheme for Spatial Data with Shuffled Differential Privacy
Li, K., Hua, Z., Xu, Y., & Liu, Z. (2024) · Mathematics · doi:10.3390/math12131934 · Cited by 4
Differential privacy: Review of improving utility through cryptography‐based technologies
Huang, W. C., Zhuo, M., Zhu, T., Zhou, S., & Liao, Y. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7565 · Cited by 4
A Review on Federated Learning approach in Artificial Intelligence
Govindwar, G. D., & Dhande, S. S. (2022) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea54992.2022.10010798 · Cited by 4
(Private) Kernelized Bandits with Distributed Biased Feedback
Li, F., Zhou, X., & Ji, B. (2023) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3579318 · Cited by 4
Crypt$ε$: Crypto-Assisted Differential Privacy on Untrusted ServersPreprint
Chowdhury, A. R., Wang, C., He, X., Machanavajjhala, A., & Jha, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07756 · Cited by 12
Communication-privacy-accuracy trade-offs in federated learning for non-IID data with shuffle model
Xiao, D., Fan, X., Chen, L., Li, M., & Zhang, M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113872 · Cited by 3
Fedmkd: multi-teacher knowledge distillation for communication-efficient federated learning
Sun, H., Pei, J., Xu, X.-Q., Xue, R., Zhao, L., Sun, Q., Li, L., & Wang, L. (2025) · Cluster Computing · doi:10.1007/s10586-025-05392-z · Cited by 3
YSZ-Based In₂O₃/SnO₂ Electrochemical Sensing Device for Highly Selective Tracking Hexanal
Xu, Y., Zhang, X., Lin, J., Wu, Y., Hòa, N. Đ., Hiếu, N. V., Ganeev, A. A., Jouyban, A., Zhou, M., Cui, D., et al. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3510102 · Cited by 3
RM 2 : Answer Counting Queries Efficiently under Shuffle Differential Privacy
Luo, Q., Yu, J., Dong, W., Xu, Q., Yang, C., & Yi, K. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725415 · Cited by 3
Privacy-Preserving Data Aggregation with Probabilistic Range Validation
Dekker, F. W., & Erkin, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78375-4_4 · Cited by 3
“In fact, with Google having deployed a custom PDA protocol for telemetry purposes in its Google Chrome browser [11, 30], the practicality of PDA protocols is evident.”
MethodTotal variation distance privacy: Accurately measuring inference attacks and improving utility
Jia, J., Tan, C., Liu, Z., Li, X., Liu, Z., Lv, S., & Dong, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.037 · Cited by 3
Privacy-Preserving Data Aggregation in IoTs: A Randomize-then-Shuffle Paradigm
Wang, Z., Tao, J., & Zou, D. (2023) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2023-spring57618.2023.10199427 · Cited by 3
A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy
Sébert, A. G., Zuber, M., Stan, O., Sirdey, R., & Gouy‐Pailler, C. (2023) · Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC) · doi:10.1145/3605759.3625258 · Cited by 3
K Privacy: Towards improving privacy strength while preserving utility
Joy, J., Gray, D., McGoldrick, C., & Gerla, M. (2018) · Ad Hoc Networks · doi:10.1016/j.adhoc.2018.05.009 · Cited by 3
Desire : Leveraging the Best of Centralized and Decentralized Contact Tracing Systems
Boutet, A., Castelluccia, C., Cunche, M., Lauradou, C., Roca, V., Baud, A., & Raverdy, P.-G. (2021) · Digital Threats Research and Practice · doi:10.1145/3480467 · Cited by 3
“We considered 100 Pet tokens per declaration and we considered two memory oblivious shuffling schemes: a sorting and a permutation-based approach with different complexities [12].”
MethodTight Bounds for Differentially Private Anonymized Histograms
Manurangsi, P. (2022) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611977066.14 · Cited by 3
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
Alon, B., Naor, M., Omri, E., & Stemmer, U. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-68397-8_3 · Cited by 3
Separation of Powers in Federated LearningPreprint
Cheng, P.-C., Eykholt, K., Gu, Z., Jamjoom, H. T., Jayaram, K. R., Valdez, E., & Verma, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.09400 · Cited by 3
“For example, Software Guard Extensions (SGX) has been leveraged to support secure model inference [15, 52], privacy-preserving multipartymachine learning [16, 20, 21, 40, 43], and analytics on sensitive data [4, 9, 46, 64].”
BackgroundUser-Level Private Learning via Correlated SamplingPreprint
Ghazi, B., Kumar, R., & Manurangsi, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11208 · Cited by 3
Restrictively Self-sampled and Compressed Local Differential Privacy in Federated LearningPreprint
Zhao, J., Li, Y., Zhang, R., Song, W., Dong, R., & Matwin, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1891162/v1 · Cited by 4
How Good Are Privacy Guarantees? Platform Architecture and Violation of User Privacy
Acemoğlu, D., Fallah, A., Makhdoumi, A., Malekian, A., & Ozdaglar, A. (2023) · National Bureau of Economic Research · doi:10.3386/w31413 · Cited by 3
Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response
Biswas, S., Jung, K., & Catuscia Palamidessi, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57537-2_27 · Cited by 2
“In order to address the problem of the loss of utility in the local model, an intermediate paradigm between the central and the local models, known as the shuffle model (SM) of differential privacy, was recently proposed [5].”
BackgroundInfluentialHybrid aggregation for federated learning under blockchain framework
Li, X., Wu, G., Yao, L., & Geng, S. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.06.009 · Cited by 2
Enhanced Privacy Bound for Shuffle Model with Personalized Privacy
Liu, Y., Liu, Y., Xiong, L., Gu, Y., & Chen, H. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679911 · Cited by 2
Side-Channel Attacks and New Principles in the Shuffle Model of Differential Privacy
Wang, S., Li, J., Dong, C., Li, J., Zhou, Z., Wang, D., & Wen, Z. A. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3571661 · Cited by 2
Distributed Differentially Private Ranking Aggregation
Song, B., Lan, Q., Li, Y., & Li, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05933-9_19 · Cited by 2
“Shuffle model can be used in intermediate nodes to realise DDP, and the protocol P was proposed in [1].”
BackgroundMethodInfluentialEfficient Oblivious Permutation via the Waksman Network
Holland, W., Ohrimenko, O., & Wirth, A. I. (2022) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3488932.3497761 · Cited by 2
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model
Kobbi Nissim, B., & Yan, C. (2022) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.805 · Cited by 2
Distributed Differentially Private Ranking Aggregation
Lan, Q., Song, B., Li, Y., & Li, G. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3225096 · Cited by 2
“The shuffle model can be used in intermediate nodes to realize DDP, and the protocol P was proposed in [18].”
BackgroundMethodInfluentialFingerprint Attack: Client De-Anonymization in Federated Learning
Xu, Q., Cohn, T., & Ohrimenko, O. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230590 · Cited by 2
“Anonymizing client identity is believed to defend against such linkage attacks and amplify the privacy guarantees in distributed and federated learning settings, as data of an individual client is “concealed” among the data of other clients [21, 11, 3].”
BackgroundMethodInfluentialDifferentially Private Multi-Site Treatment Effect Estimation
Koga, T., Chaudhuri, K., & Page, D. I. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00030 · Cited by 6
Yan, P., Murray, T., Ohrimenko, O., Pham, V.-T., & Sison, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71162-6_10 · Cited by 2
Yang, Q., Du, X., Wu, X., Wang, W., Liu, A., & Wang, S. (2024) · Cybersecurity · doi:10.1186/s42400-024-00293-x · Cited by 2
“To effectively balance the accuracy and privacy of the model, SDP-FL ( Bittau et al. 2017; Erlingsson et al. 2020; Ghazi et al. 2019, 2021; Liu et al. 2021; Scott et al. 2022) has been developed.”
Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning
Wang, S., Zeng, S., Li, J., Huang, S., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app15031595 · Cited by 2
Privacy-Enabled Recommendations for Software Vulnerabilities
Karlsson, L., & Paladi, N. (2019) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00111 · Cited by 2
“The Prochlo [28] implementation likewise uses Intel SGX enclaves to implement the the Encode, Shuffle, Analyze architecture for privacy-preserving software monitoring.”
MethodBUDS+: Better Privacy with Converger and Noisy Shuffling
Sengupta, P., Paul, S., & Mishra, S. (2021) · Digital Threats Research and Practice · doi:10.1145/3491259 · Cited by 2
Chen, Y., Zheng, Q., & Yan, Z. (2023) · ACM Transactions on Database Systems · doi:10.1145/3597021 · Cited by 2
“It has been opted by several previous studies [1, 3, 4, 20, 23, 46].”
BackgroundResultFrom Bounded to Unbounded: Privacy Amplification via Shuffling with Dummies
Takagi, S., Kato, F., Cao, Y., & Yoshikawa, M. (2023) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf57540.2023.00034 · Cited by 2
“[3] reported on the utility limitation according to their experience, stating that it was due to the excessively strong constraint of LDP, which is the main motivation of the shuffling model (see Section II-D).”
BackgroundMethodInfluentialOnline/Offline and History Indexing Identity-Based Fuzzy Message Detection
Wang, Z., Liu, F., Yiu, S., & Lan, L. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3309477 · Cited by 2
“mous data documents delivery mechanism [1], [2], [9] which transmits the confidential data documents without revealing”
BackgroundSigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation
Trivedi, D., Boudguiga, A., Kaaniche, N., & Triandopoulos, N. (2023) · Cryptography · doi:10.3390/cryptography7040052 · Cited by 2
Non-Interactive Anonymous Router with Quasi-Linear Router Computation
Fernando, R., Shi, E., Soni, P., Vanjani, N., & Waters, B. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48621-0_3 · Cited by 2
On Heuristic Models, Assumptions, and Parameters
Judson, S., & Joan Feigenbaum, A. (2025) · ACM Journal on Responsible Computing · doi:10.1145/3735562 · Cited by 2
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster
Canonne, C. L., & Lyu, H. (2022) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611977066.13 · Cited by 2
“Definition 2 (Local Differential Privacy [26, 36]).”
InfluentialThe Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential NoisePreprint
Ding, Z., Kifer, D., Saghaian, M. N. E. S., Steinke, T., Wang, Y., Xiao, Y., & Zhang, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.07260 · Cited by 2
“Differential privacy is by now considered a de facto standard for protecting confi-dentiality of user data in statistical applications and has been adopted by organizations such as Google [15, 3], Apple [26], Microsoft [9] and the U.S. Census Bureau [21, 16, 1].”
MethodA Shuffling Framework for Local Differential PrivacyPreprint
Meehan, C., Chowdhury, A. R., Chaudhuri, K., & Jha, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06603 · Cited by 2
“Strategy 2 corresponds to the recently introduced shuffle DP model, such as Google’s Prochlo (Bittau et al., 2017b).”
MethodInfluentialSelective MPC: Distributed Computation of Differentially Private Key-Value StatisticsPreprint
Humphries, T., Mahdavi, R. A., Veitch, S., & Kerschbaum, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.12407 · Cited by 2
Federated Learning of Artificial Neural Networks
Kiss, P. T. (2022) · Eötvös Loránd Tudományegyetem · doi:10.15476/elte.2021.124 · Cited by 2
DiApprox: Differential Privacy-based Online Range Queries Approximation for Multidimensional Data
Laouir, A. E., & Abdessamad Imine, A. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636070 · Cited by 1
Nebula: Efficient, Private and Accurate Histogram Estimation
Shamsabadi, A. S., Snyder, P., Giles, R., Bellet, A., & Haddadi, H. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744789 · Cited by 1
FLDS: differentially private federated learning with double shufflers
Qi, Q., Yang, X., Hu, C., Tang, P., Su, Z., & Guo, S. (2024) · The Computer Journal · doi:10.1093/comjnl/bxae122 · Cited by 1
Differential Private Data Stream Analytics in the Local and Shuffle Models
Wang, S., Li, J., Peng, Y., Chen, K., Yang, W., Jiang, H. L., & Li, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3559621 · Cited by 1
Computationally Differentially Private Inner-Product Protocols Imply Oblivious Transfer
Haitner, I., Mazor, N., Silbak, J., Tsfadia, E., & Yan, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01884-7_6 · Cited by 1
Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments
Singh, B. C., Hossain, M. J., Dı́az, R., Roy, S., Mukkamala, R., & Shetty, S. S. (2025) · IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) · doi:10.1109/mobilecloud66020.2025.00007 · Cited by 1
Wang, Y., Yang, T., & Xiong, C. (2025) · Expert Systems · doi:10.1111/exsy.70166 · Cited by 1
“DP is typically implemented in three main variants: standard differential privacy (centralised) (Dwork 2006), local differential privacy (LDP) (Wang et al. 2019), and shuffle-based differential privacy (Bittau et al. 2017), each addressing different privacy requirements and deployment scenarios.”
Fu, M., Hu, C., Li, Y., Zhang, C., & Zhu, L. (2026) · IEEE Wireless Communications · doi:10.1109/mwc.2025.3631608 · Cited by 1
Defense against Poisoning Attacks under Shuffle-DP
Wang, S., Luo, Q., Hu, Y., Wang, L., Xu, Q., Yang, C., Qin, Z., Ren, K., & Dong, W. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3786638 · Cited by 1
Giura, P., & Jim, T. (2018) · International Conference on the Internet of Things (IoT) · doi:10.1145/3277593.3277611 · Cited by 1
“For example, the web-browser monitoring and privacy-preserving aggregation of Rappor [6] and Prochlo [2] could easily be adapted to Sapphire.”
MethodExponential Separations in Local Privacy
Joseph, M., Mao, J., & Roth, A. (2022) · ACM Transactions on Algorithms · doi:10.1145/3459095 · Cited by 1
Objective-Aware Reputation-Enabled Blockchain-Based Federated Learning
Rostami, S. M., Samet, S., & Kobti, Z. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-21229-1_24 · Cited by 1
Information Ratio of Unicyclic Graphs
Gyarmati, M. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2023.3322601 · Cited by 1
“Moreover, secret sharing serves as a fundamental building tool for secure multi-party computation, it can be used for multi-signatures [3], secure aggregations [4], secure shuffling [5], attribute-based encryption and augmenting the level of secrecy in various other protocols.”
BackgroundCoStricTor: Collaborative HTTP Strict Transport Security in Tor Browser
Davitt, K., Ristea, D., Russell, D., & Murdoch, S. J. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2024-0020 · Cited by 1
Snatch: Online Streaming Analytics at the Network Edge
Xiao, Y., Zhao, Y., Lin, S., & Kuzmanovic, A. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3629577 · Cited by 1
Differentially Private Selection from Secure Distributed Computing
Damgård, I., Keller, H., Nelson, B., Orlandi, C., & Pagh, R. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645435 · Cited by 1
GoCrowd: Obliviously Aggregating Crowd Wisdom With Quality Awareness in Crowdsourcing
Liu, X., Zheng, Y., Yuan, X., & Yi, X. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3415003 · Cited by 1
Reconstruction Attacks on Aggressive Relaxations of Differential Privacy
Protivash, P., Durrell, J., Kifer, D., Ding, Z., & Zhang, D. (2024) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.871 · Cited by 1
Conan: Distributed Proofs of Compliance for Anonymous Data Collection
Zhou, M., Fanti, G. C., & Shi, E. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690264 · Cited by 1
Multidimensional categorical data collection under shuffled differential privacy
Wang, N., Zhuang, J., Wang, Z., Wei, Z., Gu, Y., Tang, P., & Yu, G. (2025) · Computers & Security · doi:10.1016/j.cose.2024.104301 · Cited by 1
Towards Learning on Vertically Partitioned Data with Distributed Differential Privacy
Bao, E., Wei, F., Yang, Y., Xiao, X., Pang, T., & Du, C. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00161 · Cited by 1
Differentially-Private Sublinear-Time Clustering
Blocki, J., Grigorescu, E., & Mukherjee, T. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9518014 · Cited by 1
“Preserving privacy in data collection and distribution have long been a concern for industrial and governmental agencies, who are now rapidly adopting privacy standards and policies [22], [11], [6], [13].”
BackgroundOblivious Stash ShufflePreprint
Maniatis, P., Mironov, I., & Talwar, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.07553 · Cited by 1
Self-citationthe Connection between Cryptography and Differential Privacy: a Survey.Preprint
Huang, W. C., Zhou, S., Zhu, T., & Liao, Y. (2020) · arXiv (Cornell University) · Cited by 1
Huang, W. C., Zhou, S., Zhu, T., & Liao, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00976 · Cited by 1
“Papers such as [34] [20] [59] find that combining local differential privacywith anonymous communication can amplify the utility guarantee of local differential privacy.”
BackgroundMethodInfluentialPrivately Learning Mixtures of Axis-Aligned GaussiansPreprint
Aden-Ali, I., Ashtiani, H., & Liaw, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02162 · Cited by 2
“Differential privacy is the de facto standard for modern private analysis and has seen widespread impact in both industry and government [12, 22, 28, 29, 39].”
BackgroundSTAR: Distributed Secret Sharing for Private Threshold Aggregation Reporting.Preprint
Davidson, A., Snyder, P., Quirk, E. B., Genereux, J., & Livshits, B. (2021) · arXiv (Cornell University) · Cited by 1
The Privacy Elasticity of Behavior: Conceptualization and ApplicationPreprint
Dekel, I., Cummings, R., Heffetz, O., & Ligett, K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4153108 · Cited by 1
Diyora, V., & Savani, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3626763/v1 · Cited by 1
Ohrimenko, O. (2025) · Springer eBooks · doi:10.1007/978-3-030-71522-9_1791 · Cited by 1
Exponential Separations in Local Differential PrivacyPreprint
Joseph, M., Mao, J., & Roth, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00813
“Differential privacy’s mathematical and practical appeal has driven the creation of a large body of work (see Dwork and Roth [16] and Vadhan [27] for surveys) and increasing industrial and governmental adoption [1, 3, 6, 13, 18].”
BackgroundEfficient Private Machine Learning by Differentiable Random TransformationsPreprint
Zheng, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07758
“And the ESA architecture [17] uses local differential privacy to ensure the worst-case privacy when all other parties are colluding together.”
BackgroundIntertwining Order Preserving Encryption and Differential Privacy.Preprint
Chowdhury, A. R., Ding, B., Jha, S., Liu, W., & Zhou, J. (2020) · arXiv (Cornell University)
Metadata
- Authors
- Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., & Seefeld, B.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Proceedings of the 26th Symposium on Operating Systems Principles
- Publisher
- ACM
- Published
- 2017-10-14
- Pages
- 441-459
- OpenAlex
- W2761138375
- Cited by
- 378
- References
- 50
- Citations by year
- 2026: 22 · 2025: 37 · 2024: 55 · 2023: 46 · 2022: 50 · 2021: 70 · 2020: 46 · 2019: 36 · 2018: 12 · 2017: 4
- FWCI
- 11.96
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGOPS Symposium on Operating Systems Principles (SOSP) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- TSpace (University of Toronto) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- user privacy, privacy-preserving monitoring, telemetry, error reporting, demographic profiling, system architecture
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