@misc{cryptoeprint:2024/1894,
        author = {Sofiane Azogagh and Marc-Olivier Killijian and Félix Larose-Gervais},
        title = {A non-comparison oblivious sort and its application to private k-{NN}},
        howpublished = {Cryptology {ePrint} Archive, Paper 2024/1894},
        year = {2024},
        url = {https://eprint.iacr.org/2024/1894}
        }
      

          @inproceedings{azogagh2024crypto,
            title={Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning},
            author={Azogagh, Sofiane and Birba, Zelma Aubin and Gambs, S{\'e}bastien and Killijian, Marc-Olivier},
            booktitle={Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy},
            pages={199--210},
            year={2024}
          }
      

          @inproceedings{azogagh2024oblivious,
            title={Oblivious Turing Machine},
            author={Azogagh, Sofiane and Delfour, Victor and Killijian, Marc-Olivier},
            booktitle={2024 19th European Dependable Computing Conference (EDCC)},
            pages={17--24},
            year={2024},
            organization={IEEE}
          }
      

          @inproceedings{azogagh2022probonite,
            title={Probonite: Private one-branch-only non-interactive decision tree evaluation},
            author={Azogagh, Sofiane and Delfour, Victor and Gambs, S{\'e}bastien and Killijian, Marc-Olivier},
            booktitle={Proceedings of the 10th Workshop on Encrypted Computing \& Applied Homomorphic Cryptography},
            pages={23--33},
            year={2022}
          }
      

          @misc{cryptoeprint:2024/1935,
            author = {Sofiane Azogagh and Zelma Aubin Birba and Marc-Olivier Killijian and Félix Larose-Gervais},
            title = {{RevoLUT} : Rust Efficient Versatile Oblivious Look-Up-Tables},
            howpublished = {Cryptology {ePrint} Archive, Paper 2024/1935},
            year = {2024},
            url = {https://eprint.iacr.org/2024/1935}
          }
      

                                    @inproceedings{10.1145/3711896.3736988,
                                        author = {Azogagh, Sofiane and Birba, Zelma Aubin and Desharnais, Jos\'{e}e and Gambs, S\'{e}bastien and Killijian, Marc-Olivier and Tawbi, Nadia},
                                        title = {GRAND : Graph Reconstruction from Potential Partial Adjacency and Neighborhood Data},
                                        year = {2025},
                                        isbn = {9798400714542},
                                        publisher = {Association for Computing Machinery},
                                        address = {New York, NY, USA},
                                        url = {https://doi.org/10.1145/3711896.3736988},
                                        doi = {10.1145/3711896.3736988},
                                        abstract = {Cryptographic approaches, such as secure multiparty computation, can be used to securely compute a function of a distributed graph without centralizing the data of each participant. However, the output of the protocol can leak sensitive information about the structure of the original graph. In particular, we propose an approach by which an adversary observing the result of a private protocol for the computation of the number of common neighbors between all pairs of vertices, can reconstruct the adjacency matrix of the graph. In fact, this can only be done up to co-squareness, a notion we introduce, as two different graphs can have the same matrix of common neighbors. To realize this, we consider two adversary models, one who observes the common neighbors matrix only and a more informed one that has partial knowledge of the original graph. Our results demonstrate that, from their common neighbors matrix, graphs can be reconstructed with high accuracy (up to co-squareness). The proposed reconstruction is also interesting in itself from the point of view of graph theory.},
                                        booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
                                        pages = {47–58},
                                        numpages = {12},
                                        keywords = {common neighbor matrix, edge prediction, graph reconstruction, private computing},
                                        location = {Toronto ON, Canada},
                                        series = {KDD '25}
                                        }