3. Anciaux, N., Bonnet, P., Bouganim, L., Nguyen, B., et al.: Personal Data Manage-
ment Systems: The Security and Functionality Standpoint. Information Systems
(2019)

4. Anciaux, N., Bouganim, L., Pucheral, P., Sandu-Popa, I., et al.: Personal Database Security and Trusted Execution Environments: A Tutorial at the Crossroads. PVLDB (2019)

5. ARM, Building a Secure System using TrustZone Technology, (2008). https://perma.cc/WE2E-WV2A

6. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., et al.: How to backdoor federated learning. In: International conference on artificial intelligence and statistics (2020)

7. Basu, A., Fleming, S., Stanier, J., Naicken, S., et al.: The state of peer-to-peer network simulators. ACM Computing Surveys (CSUR) (2013)

8. Bater, J., Elliott, G., Eggen, C., Goel, S., et al.: SMCQL: Secure Query Processing for Private Data Networks. PVLDB (2017)

9. Bellet, A., Guerraoui, R., Taziki, M., Tommasi, M.: Personalized and Private Peer-to-Peer Machine Learning. In: AIStat (2018)

10. Beutel, D.J., Topal, T., Mathur, A., Qiu, X., et al.: Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)

11. Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., et al.: Prochlo: Strong privacy for analytics in the crowd. In: Proceedings of the 26th symposium on operating systems principles (2017)

12. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., et al.: Practical Secure Aggregation for Privacy-Preserving Machine Learning. In: ACM CCS (2017)

13. Bouganim, L., Loudet, J., Sandu Popa, I.: Highly Distributed and Privacy-Preserving Queries on Personal Data Management Systems. The VLDB Journal (2023)

14. Brasser, F., Müller, U., Dmitrienko, A., Kostiainen, K., et al.: Software Grand Exposure: SGX Cache Attacks Are Practical. In: 11th USENIX Workshop on Offensive Technologies (2017)

15. Bulck, J.V., Minkin, M., Weisse, O., Genkin, D., et al.: Foreshadow: Extracting the Keys to the Intel SGX Kingdom with Transient Out-of-Order Execution. In: 27th USENIX Security Symposium (2018)

16. Cormode, G., Kulkarni, T., Srivastava, D.: Answering Range Queries Under Local Differential Privacy. PVLDB (2019)

17. Corrigan-Gibbs, H., Boneh, D.: Prio: Private, Robust, and Scalable Computation of Aggregate Statistics. In: NSDI (2017)

18. Costan, V., Devadas, S.: Intel SGX Explained, (2016). Cryptology ePrint Archive.

19. Cozy Cloud, Cozy Cloud (See https://cozy.io/fr/), (2023)

20. Ding, B., Kulkarni, J., Yekhanin, S.: Collecting Telemetry Data Privately. In: Advances in Neural Information Processing Systems (2017)

21. Dong, Y., Chen, X., Li, K., Wang, D., et al.: FLOD: Oblivious Defender for Private Byzantine-Robust Federated Learning with Dishonest-Majority. In: ESORICS (2021)

22. Dwork, C.: Differential Privacy. In: Automata, Languages and Programming (2006)

23. Ebrahim, M., Khan, S., Hasan Mohani, S.S.U.: Peer-to-Peer Network Simulators: an Analytical Review. Asian Journal of Engineering, Sciences & Technology (2012)

24. Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. CCS '14 (2014)

25. Eskandarian, S., Zaharia, M.: ObliDB: Oblivious Query Processing for Secure Databases. Proc. VLDB Endow. (2019)
3. Anciaux, N., Bonnet, P., Bouganim, L., Nguyen, B., et al.: Personal Data Manage-
ment Systems: The Security and Functionality Standpoint. Information Systems
(2019)

4. Anciaux, N., Bouganim, L., Pucheral, P., Sandu-Popa, I., et al.: Personal Database Security and Trusted Execution Environments: A Tutorial at the Crossroads. PVLDB (2019)

5. ARM, Building a Secure System using TrustZone Technology, (2008). https://perma.cc/WE2E-WV2A

6. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., et al.: How to backdoor federated learning. In: International conference on artificial intelligence and statistics (2020)

7. Basu, A., Fleming, S., Stanier, J., Naicken, S., et al.: The state of peer-to-peer network simulators. ACM Computing Surveys (CSUR) (2013)

8. Bater, J., Elliott, G., Eggen, C., Goel, S., et al.: SMCQL: Secure Query Processing for Private Data Networks. PVLDB (2017)

9. Bellet, A., Guerraoui, R., Taziki, M., Tommasi, M.: Personalized and Private Peer-to-Peer Machine Learning. In: AIStat (2018)

10. Beutel, D.J., Topal, T., Mathur, A., Qiu, X., et al.: Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)

11. Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., et al.: Prochlo: Strong privacy for analytics in the crowd. In: Proceedings of the 26th symposium on operating systems principles (2017)

12. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., et al.: Practical Secure Aggregation for Privacy-Preserving Machine Learning. In: ACM CCS (2017)

13. Bouganim, L., Loudet, J., Sandu Popa, I.: Highly Distributed and Privacy-Preserving Queries on Personal Data Management Systems. The VLDB Journal (2023)

14. Brasser, F., Müller, U., Dmitrienko, A., Kostiainen, K., et al.: Software Grand Exposure: SGX Cache Attacks Are Practical. In: 11th USENIX Workshop on Offensive Technologies (2017)

15. Bulck, J.V., Minkin, M., Weisse, O., Genkin, D., et al.: Foreshadow: Extracting the Keys to the Intel SGX Kingdom with Transient Out-of-Order Execution. In: 27th USENIX Security Symposium (2018)

16. Cormode, G., Kulkarni, T., Srivastava, D.: Answering Range Queries Under Local Differential Privacy. PVLDB (2019)

17. Corrigan-Gibbs, H., Boneh, D.: Prio: Private, Robust, and Scalable Computation of Aggregate Statistics. In: NSDI (2017)

18. Costan, V., Devadas, S.: Intel SGX Explained, (2016). Cryptology ePrint Archive.

19. Cozy Cloud, Cozy Cloud (See https://cozy.io/fr/), (2023)

20. Ding, B., Kulkarni, J., Yekhanin, S.: Collecting Telemetry Data Privately. In: Advances in Neural Information Processing Systems (2017)

21. Dong, Y., Chen, X., Li, K., Wang, D., et al.: FLOD: Oblivious Defender for Private Byzantine-Robust Federated Learning with Dishonest-Majority. In: ESORICS (2021)

22. Dwork, C.: Differential Privacy. In: Automata, Languages and Programming (2006)

23. Ebrahim, M., Khan, S., Hasan Mohani, S.S.U.: Peer-to-Peer Network Simulators: an Analytical Review. Asian Journal of Engineering, Sciences & Technology (2012)

24. Erlingsson, Ú., Pihur, V., Korolova, A.: RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. CCS '14 (2014)

25. Eskandarian, S., Zaharia, M.: ObliDB: Oblivious Query Processing for Secure Databases. Proc. VLDB Endow. (2019)
26. European Commission, Proposal for a Regulation on European data governance (Data Governance Act). [eur-lex], (2020).

27. Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., et al.: SAFELearn: Secure Aggregation for Private Federated Learning. In: IEEE SPW (2021)

28. Ghazi, B., Manurangsi, P., Pagh, R., Velingker, A.: Private aggregation from fewer anonymous messages. In: EUROCRYPT (2020)

29. Gil, T., Kaashoek, F., Li, J., Morris, R., Stribling, J.: P2PSim, a simulator for peer-to-peer protocols, (2003).

30. Gueron, S.: Memory Encryption for General-Purpose Processors. IEEE Security Privacy **14** (2016)

31. Guo, X., Liu, Z., Li, J., Gao, J., et al.: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning. IEEE Trans. Inf. Forensics Secur. (2021)

32. Gupta, P., Li, Y., Mehrotra, S., Panwar, N., et al.: Obscure: Information-Theoretic Oblivious and Verifiable Aggregation Queries. PVLDB (2019)

33. Hayek, R., Raschia, G., Valduriez, P., Mouaddib, N.: Summary management in P2P systems. In: EDBT (2008)

34. Huba, D., Nguyen, J., Malik, K., Zhu, R., et al.: Papaya: Practical, private, and scalable federated learning. Proceedings of Machine Learning and Systems (2022)

35. Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., et al.: What Can We Learn Privately? SIAM Journal on Computing (2011)

36. Li, N., Li, T., Venkatasubramanian, S.: t Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: ICDE (2007)

37. Loudet, J., Sandu-Popa, I., Bouganim, L.: DISPERS: Securing Highly Distributed Queries on Personal Data Management Systems. PVLDB (2019)

38. Loudet, J., Sandu-Popa, I., Bouganim, L.: SEP2P: Secure and Efficient P2P Personal Data Processing. In: EDBT (2019)

39. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-Diversity: Privacy beyond k-Anonymity. ACM Transactions on Knowledge Discovery from Data (2007)

40. Mansouri, M., Önen, M., Jaballah, W.B., Conti, M.: SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning. PETS (2023)

41. McMahan, B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-Efficient Learning of Deep Networks from Decentralized Data. In: PMLR (2017)

42. Merkle, R.C.: A Digital Signature Based on a Conventional Encryption Function. In: CRYPTO (1987)

43. Mirval, J.: FLP2P Peer-to-peer Simulator, https://github.com/cozy/dissec-cozy/tree/master/simulation.

44. Mirval, J., Bouganim, L., Sandu Popa, I.: Federated learning on personal data management systems: Decentralized and reliable secure aggregation protocols. In: SSDBM (2023)

45. Mirval, J., Bouganim, L., Sandu-Popa, I.: Practical Fully-Decentralized Secure Aggregation for Personal Data Management Systems. In: SSDBM (2021)

46. Montresor, A., Jelasity, M.: PeerSim: A scalable P2P simulator. In: P2P (2009)

47. Naicken, S., Livingston, B., Basu, A., Rodhetbhai, S., et al.: The state of peer-to-peer simulators and simulations. ACM SIGCOMM (2007)

48. Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., et al.: Federated learning with buffered asynchronous aggregation. In: AISTATS (2022)

49. nPerf, Baromètre des connexions Internet fixes en France métropolitaine, (2020). https://perma.cc/DP8V-5ABT
26. European Commission, Proposal for a Regulation on European data governance (Data Governance Act). [eur-lex], (2020).

27. Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., et al.: SAFELearn: Secure Aggregation for Private Federated Learning. In: IEEE SPW (2021)

28. Ghazi, B., Manurangsi, P., Pagh, R., Velingker, A.: Private aggregation from fewer anonymous messages. In: EUROCRYPT (2020)

29. Gil, T., Kaashoek, F., Li, J., Morris, R., Stribling, J.: P2PSim, a simulator for peer-to-peer protocols, (2003).

30. Gueron, S.: Memory Encryption for General-Purpose Processors. IEEE Security Privacy **14** (2016)

31. Guo, X., Liu, Z., Li, J., Gao, J., et al.: VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning. IEEE Trans. Inf. Forensics Secur. (2021)

32. Gupta, P., Li, Y., Mehrotra, S., Panwar, N., et al.: Obscure: Information-Theoretic Oblivious and Verifiable Aggregation Queries. PVLDB (2019)

33. Hayek, R., Raschia, G., Valduriez, P., Mouaddib, N.: Summary management in P2P systems. In: EDBT (2008)

34. Huba, D., Nguyen, J., Malik, K., Zhu, R., et al.: Papaya: Practical, private, and scalable federated learning. Proceedings of Machine Learning and Systems (2022)

35. Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., et al.: What Can We Learn Privately? SIAM Journal on Computing (2011)

36. Li, N., Li, T., Venkatasubramanian, S.: t Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: ICDE (2007)

37. Loudet, J., Sandu-Popa, I., Bouganim, L.: DISPERS: Securing Highly Distributed Queries on Personal Data Management Systems. PVLDB (2019)

38. Loudet, J., Sandu-Popa, I., Bouganim, L.: SEP2P: Secure and Efficient P2P Personal Data Processing. In: EDBT (2019)

39. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-Diversity: Privacy beyond k-Anonymity. ACM Transactions on Knowledge Discovery from Data (2007)

40. Mansouri, M., Önen, M., Jaballah, W.B., Conti, M.: SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning. PETS (2023)

41. McMahan, B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-Efficient Learning of Deep Networks from Decentralized Data. In: PMLR (2017)

42. Merkle, R.C.: A Digital Signature Based on a Conventional Encryption Function. In: CRYPTO (1987)

43. Mirval, J.: FLP2P Peer-to-peer Simulator, https://github.com/cozy/dissec-cozy/tree/master/simulation.

44. Mirval, J., Bouganim, L., Sandu Popa, I.: Federated learning on personal data management systems: Decentralized and reliable secure aggregation protocols. In: SSDBM (2023)

45. Mirval, J., Bouganim, L., Sandu-Popa, I.: Practical Fully-Decentralized Secure Aggregation for Personal Data Management Systems. In: SSDBM (2021)

46. Montresor, A., Jelasity, M.: PeerSim: A scalable P2P simulator. In: P2P (2009)

47. Naicken, S., Livingston, B., Basu, A., Rodhetbhai, S., et al.: The state of peer-to-peer simulators and simulations. ACM SIGCOMM (2007)

48. Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., et al.: Federated learning with buffered asynchronous aggregation. In: AISTATS (2022)

49. nPerf, Baromètre des connexions Internet fixes en France métropolitaine, (2020). https://perma.cc/DP8V-5ABT
50. Oleksenko, O., Trach, B., Krahn, R., Silberstein, M., et al.: Varys: Protecting SGX Enclaves from Practical Side-Channel Attacks. In: USENIX ATC, Boston, MA (2018)

51. Pilet, A.B., Frey, D., Taiani, F.: Robust Privacy-Preserving Gossip Averaging. In: SSS (2019)

52. Przydatek, B., Song, D., Perrig, A.: SIA: Secure Information Aggregation in Sensor Networks. In: SenSys (2003)

53. Ratnasamy, S., Francis, P., Handley, M., Karp, R.M., et al.: A Scalable Content-Addressable Network. In: ACM SIGCOMM (2001)

54. Reynolds, P., Vahdat, A.: Efficient Peer-to-Peer Keyword Searching. In: Middleware (2003)

55. Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. Foundations of secure computation (1978)

56. Sabater, C., Bellet, A., Ramon, J.: An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging. JMLR (2022)

57. Sabt, M., Achemlal, M., Bouabdallah, A.: Trusted Execution Environment: What It is, and What It is Not. In: Trustcom (2015)

58. Sandu-Popa, I., Ton-That, D.H., Zeitouni, K., Borcea, C.: Mobile Participatory Sensing with Strong Privacy Guarantees using Secure Probes. GeoInformatica (2021)

59. van Schaik, S., Kwong, A., Genkin, D., Yarom, Y.: SGAxe: How SGX Fails in Practice, (2020). https://sgaxeattack.com/

60. van Schaik, S., Minkin, M., Kwong, A., Genkin, D., et al.: CacheOut: Leaking Data on Intel CPUs via Cache Evictions. In: SP (2021)

61. Schwarz, M., Weiser, S., Gruss, D., Maurice, C., et al.: Malware Guard Extension: Using SGX to Conceal Cache Attacks. In: DIMVA (2017)

62. So, J., Güler, B., Avestimehr, A.S.: Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning. JSAIT (2021)

63. So, J., He, C., Yang, C.-S., Li, S., et al.: Lightsecagg: A Lightweight and Versatile Design for Secure Aggregation in Federated Learning. MLSys (2022)

64. Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., et al.: Chord: A Scalable Peer-to-Peer Lookup Service for Internet Applications. ACM SIGCOMM (2001)

65. Sweeney, L.: k-anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. (2002)

66. Tang, C., Dwarkadas, S.: Hybrid Global-Local Indexing for Efficient Peer-to-Peer Information Retrieval. In: NSDI (2004)

67. Tang, C., Xu, Z., Dwarkadas, S.: Peer-to-Peer Information Retrieval using Self-Organizing Semantic Overlay Networks. In: ACM SIGCOMM (2003)

68. To, Q., Nguyen, B., Pucheral, P.: Private and Scalable Execution of SQL Aggregates on a Secure Decentralized Architecture. ACM TODS (2016)

69. Triastcyn, A., Faltings, B.: Federated Learning with Bayesian Differential Privacy. In: IEEE BigData (2019)

70. Wang, E., Chen, B., Chowdhury, M., Kannan, A., Liang, F.: FLINT: A Platform for Federated Learning Integration. Proceedings of Machine Learning and Systems (2023)

71. Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., et al.: Attack of the tails: Yes, you really can backdoor federated learning. NIPS (2020)

72. Warner, S.L.: Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. JASA (1965)
50. Oleksenko, O., Trach, B., Krahn, R., Silberstein, M., et al.: Varys: Protecting SGX Enclaves from Practical Side-Channel Attacks. In: USENIX ATC, Boston, MA (2018)

51. Pilet, A.B., Frey, D., Taiani, F.: Robust Privacy-Preserving Gossip Averaging. In: SSS (2019)

52. Przydatek, B., Song, D., Perrig, A.: SIA: Secure Information Aggregation in Sensor Networks. In: SenSys (2003)

53. Ratnasamy, S., Francis, P., Handley, M., Karp, R.M., et al.: A Scalable Content-Addressable Network. In: ACM SIGCOMM (2001)

54. Reynolds, P., Vahdat, A.: Efficient Peer-to-Peer Keyword Searching. In: Middleware (2003)

55. Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. Foundations of secure computation (1978)

56. Sabater, C., Bellet, A., Ramon, J.: An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging. JMLR (2022)

57. Sabt, M., Achemlal, M., Bouabdallah, A.: Trusted Execution Environment: What It is, and What It is Not. In: Trustcom (2015)

58. Sandu-Popa, I., Ton-That, D.H., Zeitouni, K., Borcea, C.: Mobile Participatory Sensing with Strong Privacy Guarantees using Secure Probes. GeoInformatica (2021)

59. van Schaik, S., Kwong, A., Genkin, D., Yarom, Y.: SGAxe: How SGX Fails in Practice, (2020). https://sgaxeattack.com/

60. van Schaik, S., Minkin, M., Kwong, A., Genkin, D., et al.: CacheOut: Leaking Data on Intel CPUs via Cache Evictions. In: SP (2021)

61. Schwarz, M., Weiser, S., Gruss, D., Maurice, C., et al.: Malware Guard Extension: Using SGX to Conceal Cache Attacks. In: DIMVA (2017)

62. So, J., Güler, B., Avestimehr, A.S.: Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning. JSAIT (2021)

63. So, J., He, C., Yang, C.-S., Li, S., et al.: Lightsecagg: A Lightweight and Versatile Design for Secure Aggregation in Federated Learning. MLSys (2022)

64. Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., et al.: Chord: A Scalable Peer-to-Peer Lookup Service for Internet Applications. ACM SIGCOMM (2001)

65. Sweeney, L.: k-anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. (2002)

66. Tang, C., Dwarkadas, S.: Hybrid Global-Local Indexing for Efficient Peer-to-Peer Information Retrieval. In: NSDI (2004)

67. Tang, C., Xu, Z., Dwarkadas, S.: Peer-to-Peer Information Retrieval using Self-Organizing Semantic Overlay Networks. In: ACM SIGCOMM (2003)

68. To, Q., Nguyen, B., Pucheral, P.: Private and Scalable Execution of SQL Aggregates on a Secure Decentralized Architecture. ACM TODS (2016)

69. Triastcyn, A., Faltings, B.: Federated Learning with Bayesian Differential Privacy. In: IEEE BigData (2019)

70. Wang, E., Chen, B., Chowdhury, M., Kannan, A., Liang, F.: FLINT: A Platform for Federated Learning Integration. Proceedings of Machine Learning and Systems (2023)

71. Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., et al.: Attack of the tails: Yes, you really can backdoor federated learning. NIPS (2020)

72. Warner, S.L.: Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. JASA (1965)
73. Yang, G., Wang, S., Wang, H.: Federated Learning with Personalized Local Differential Privacy. In: IEEE ICCCS (2021)

74. Yang, Y., Dunlap, R., Rexroad, M., Cooper, B.F.: Performance of Full Text Search in Structured and Unstructured Peer-to-Peer Systems. In: INFOCOM (2006)

75. Zhang, C., Li, S., Xia, J., Wang, W., et al.: {BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning. In: USENIX ATC (2020)

76. Zhang, Z., Wang, T., Li, N., He, S., et al.: CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy. In: ACM CCS (2018)
73. Yang, G., Wang, S., Wang, H.: Federated Learning with Personalized Local Differential Privacy. In: IEEE ICCCS (2021)

74. Yang, Y., Dunlap, R., Rexroad, M., Cooper, B.F.: Performance of Full Text Search in Structured and Unstructured Peer-to-Peer Systems. In: INFOCOM (2006)

75. Zhang, C., Li, S., Xia, J., Wang, W., et al.: {BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning. In: USENIX ATC (2020)

76. Zhang, Z., Wang, T., Li, N., He, S., et al.: CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy. In: ACM CCS (2018)