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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Verifiable Differential Privacy.\n \n \n \n \n\n\n \n Biswas, A.; and Cormode, G.\n\n\n \n\n\n\n January 2023.\n arXiv:2208.09011 [cs]\n\n\n\n
\n\n\n\n \n \n \"VerifiablePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@misc{biswas_verifiable_2023,\n\ttitle = {Verifiable {Differential} {Privacy}},\n\turl = {http://arxiv.org/abs/2208.09011},\n\tabstract = {Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de-facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of {\\textbackslash}textit\\{Verifiable Differential Privacy\\}, which requires the publishing entity to output a zero-knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.},\n\turldate = {2023-02-03},\n\tpublisher = {arXiv},\n\tauthor = {Biswas, Ari and Cormode, Graham},\n\tmonth = jan,\n\tyear = {2023},\n\tnote = {arXiv:2208.09011 [cs]},\n\tkeywords = {Computer Science - Cryptography and Security},\n}\n\n
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\n Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de-facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of \\textit\\Verifiable Differential Privacy\\, which requires the publishing entity to output a zero-knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.\n
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\n  \n 2019\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Seeker: Real-Time Interactive Search.\n \n \n \n \n\n\n \n Biswas, A.; Pham, T. T.; Vogelsong, M.; Snyder, B.; and Nassif, H.\n\n\n \n\n\n\n In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2867–2875, Anchorage AK USA, July 2019. ACM\n \n\n\n\n
\n\n\n\n \n \n \"Seeker:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{biswas_seeker_2019,\n\taddress = {Anchorage AK USA},\n\ttitle = {Seeker: {Real}-{Time} {Interactive} {Search}},\n\tisbn = {978-1-4503-6201-6},\n\tshorttitle = {Seeker},\n\turl = {https://dl.acm.org/doi/10.1145/3292500.3330733},\n\tdoi = {10.1145/3292500.3330733},\n\tabstract = {This paper introduces Seeker, a system that allows users to adaptively refine search rankings in real time, through a series of feedbacks in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: “Is this item similar to what you have in mind?” With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.},\n\tlanguage = {en},\n\turldate = {2022-05-21},\n\tbooktitle = {Proceedings of the 25th {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} \\& {Data} {Mining}},\n\tpublisher = {ACM},\n\tauthor = {Biswas, Ari and Pham, Thai T. and Vogelsong, Michael and Snyder, Benjamin and Nassif, Houssam},\n\tmonth = jul,\n\tyear = {2019},\n\tpages = {2867--2875},\n}\n\n
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\n This paper introduces Seeker, a system that allows users to adaptively refine search rankings in real time, through a series of feedbacks in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: “Is this item similar to what you have in mind?” With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.\n
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\n  \n 2017\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Adversarial principal component analysis.\n \n \n \n \n\n\n \n Pimentel-Alarcon, D. L.; Biswas, A.; and Solis-Lemus, C. R.\n\n\n \n\n\n\n In 2017 IEEE International Symposium on Information Theory (ISIT), pages 2363–2367, Aachen, Germany, June 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"AdversarialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pimentel-alarcon_adversarial_2017,\n\taddress = {Aachen, Germany},\n\ttitle = {Adversarial principal component analysis},\n\tisbn = {978-1-5090-4096-4},\n\turl = {http://ieeexplore.ieee.org/document/8006952/},\n\tdoi = {10.1109/ISIT.2017.8006952},\n\tabstract = {This paper studies the following question: where should an adversary place an outlier of a given magnitude in order to maximize the error of the subspace estimated by PCA? We give the exact location of this worst possible outlier, and the exact expression of the maximum possible error. Equivalently, we determine the information-theoretic bounds on how much an outlier can tilt a subspace in its direction. This in turn provides universal (worst-case) error bounds for PCA under arbitrary noisy settings. Our results also have several implications on adaptive PCA, online PCA, and rank-one updates. We illustrate our results with a subspace tracking experiment.},\n\tlanguage = {en},\n\turldate = {2022-05-21},\n\tbooktitle = {2017 {IEEE} {International} {Symposium} on {Information} {Theory} ({ISIT})},\n\tpublisher = {IEEE},\n\tauthor = {Pimentel-Alarcon, Daniel L. and Biswas, Aritra and Solis-Lemus, Claudia R.},\n\tmonth = jun,\n\tyear = {2017},\n\tpages = {2363--2367},\n}\n
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\n This paper studies the following question: where should an adversary place an outlier of a given magnitude in order to maximize the error of the subspace estimated by PCA? We give the exact location of this worst possible outlier, and the exact expression of the maximum possible error. Equivalently, we determine the information-theoretic bounds on how much an outlier can tilt a subspace in its direction. This in turn provides universal (worst-case) error bounds for PCA under arbitrary noisy settings. Our results also have several implications on adaptive PCA, online PCA, and rank-one updates. We illustrate our results with a subspace tracking experiment.\n
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Hands-on introduction to computer science at the freshman level.\n \n \n \n \n\n\n \n Balasubramanian, R.; York, Z.; Doran, M.; Biswas, A.; Girgin, T.; and Sankaralingam, K.\n\n\n \n\n\n\n In Proceedings of the 45th ACM technical symposium on Computer science education, of SIGCSE '14, pages 235–240, New York, NY, USA, March 2014. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"Hands-onPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{balasubramanian_hands-introduction_2014,\n\taddress = {New York, NY, USA},\n\tseries = {{SIGCSE} '14},\n\ttitle = {Hands-on introduction to computer science at the freshman level},\n\tisbn = {978-1-4503-2605-6},\n\turl = {https://doi.org/10.1145/2538862.2538889},\n\tdoi = {10.1145/2538862.2538889},\n\tabstract = {This paper details the creation of a hands-on introduction course that reflects the dramatic growth and diversity in computer science. Our aim was to enable students to get an end-to-end perspective on computer system design by building one. We report on a two-year exercise in using the Arduino platform to build a series of hands-on projects. We have used these projects in two course instances, and have obtained detailed student feedback, which we analyze and present in this paper. The instructions, code and videos developed are available open-source.},\n\turldate = {2022-05-21},\n\tbooktitle = {Proceedings of the 45th {ACM} technical symposium on {Computer} science education},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Balasubramanian, Raghuraman and York, Zachary and Doran, Matthew and Biswas, Aritra and Girgin, Timur and Sankaralingam, Karthikeyan},\n\tmonth = mar,\n\tyear = {2014},\n\tkeywords = {hands-on projects, introduction to computer science, pedagogy},\n\tpages = {235--240},\n}\n\n
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\n This paper details the creation of a hands-on introduction course that reflects the dramatic growth and diversity in computer science. Our aim was to enable students to get an end-to-end perspective on computer system design by building one. We report on a two-year exercise in using the Arduino platform to build a series of hands-on projects. We have used these projects in two course instances, and have obtained detailed student feedback, which we analyze and present in this paper. The instructions, code and videos developed are available open-source.\n
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