Pub Date : 2025-04-24DOI: 10.1109/TIT.2025.3547657
Adeel Mahmood;Aaron B. Wagner
Presents corrections to the paper, (Errata to “Channel Coding With Mean and Variance Cost Constraints”).
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Pub Date : 2025-04-24DOI: 10.1109/TIT.2025.3560573
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Pub Date : 2025-04-24DOI: 10.1109/TIT.2025.3560575
{"title":"IEEE Transactions on Information Theory Information for Authors","authors":"","doi":"10.1109/TIT.2025.3560575","DOIUrl":"https://doi.org/10.1109/TIT.2025.3560575","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 5","pages":"C3-C3"},"PeriodicalIF":2.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-24DOI: 10.1109/TIT.2025.3554064
{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TIT.2025.3554064","DOIUrl":"https://doi.org/10.1109/TIT.2025.3554064","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 4","pages":"3254-3254"},"PeriodicalIF":2.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-24DOI: 10.1109/TIT.2025.3554060
{"title":"Member ad suite","authors":"","doi":"10.1109/TIT.2025.3554060","DOIUrl":"https://doi.org/10.1109/TIT.2025.3554060","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 4","pages":"3252-3252"},"PeriodicalIF":2.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-24DOI: 10.1109/TIT.2025.3549268
{"title":"IEEE Transactions on Information Theory Information for Authors","authors":"","doi":"10.1109/TIT.2025.3549268","DOIUrl":"https://doi.org/10.1109/TIT.2025.3549268","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 4","pages":"C3-C3"},"PeriodicalIF":2.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-24DOI: 10.1109/TIT.2025.3549266
{"title":"IEEE Transactions on Information Theory Publication Information","authors":"","doi":"10.1109/TIT.2025.3549266","DOIUrl":"https://doi.org/10.1109/TIT.2025.3549266","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 4","pages":"C2-C2"},"PeriodicalIF":2.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1109/TIT.2025.3552671
Armando Angrisani;Elham Kashefi
Differential privacy provides a robust framework for protecting sensitive data, while maintaining its utility for computation. In essence, a differentially private algorithm takes as input the data of multiple parties, and returns an output disclosing minimal information about any individual party. Previous research has introduced several quantum extensions of differential privacy, with applications ranging from quantum machine learning on private classical data to quantum shadow tomography. However, the local model of quantum differential privacy – where each party is responsible for privatizing their own data at a local level – has received limited attention. This work delves into locally differentially private quantum measurements. Although any measurement can be made locally differentially private by adding noise to the outcome, we demonstrate that certain quantum measurements inherently satisfy some degree of local differential privacy for specific classes of input states. This finding has two significant implications: first, limiting the analysis to classical noise injection mechanisms may lead to suboptimal privacy-utility trade-offs for quantum data; second, the theory of differential privacy can be harnessed to further investigate the capabilities of quantum measurements. Motivated by these insights, we establish strong data processing inequalities for the quantum relative entropy under local differential privacy and apply these results to asymmetric hypothesis testing of quantum states with restricted measurements. Additionally, we prove an equivalence between quantum statistical queries and quantum differential privacy in the local model, thereby addressing an open question posed by Arunachalam et al. (2021). Finally, we consider the task of quantum multi-party computation under local differential privacy, demonstrating that parity functions can be efficiently learned in this model, whereas the corresponding classical task requires exponentially many samples.
{"title":"Quantum Differential Privacy in the Local Model","authors":"Armando Angrisani;Elham Kashefi","doi":"10.1109/TIT.2025.3552671","DOIUrl":"https://doi.org/10.1109/TIT.2025.3552671","url":null,"abstract":"Differential privacy provides a robust framework for protecting sensitive data, while maintaining its utility for computation. In essence, a differentially private algorithm takes as input the data of multiple parties, and returns an output disclosing minimal information about any individual party. Previous research has introduced several quantum extensions of differential privacy, with applications ranging from quantum machine learning on private classical data to quantum shadow tomography. However, the local model of quantum differential privacy – where each party is responsible for privatizing their own data at a local level – has received limited attention. This work delves into locally differentially private quantum measurements. Although any measurement can be made locally differentially private by adding noise to the outcome, we demonstrate that certain quantum measurements inherently satisfy some degree of local differential privacy for specific classes of input states. This finding has two significant implications: first, limiting the analysis to classical noise injection mechanisms may lead to suboptimal privacy-utility trade-offs for quantum data; second, the theory of differential privacy can be harnessed to further investigate the capabilities of quantum measurements. Motivated by these insights, we establish strong data processing inequalities for the quantum relative entropy under local differential privacy and apply these results to asymmetric hypothesis testing of quantum states with restricted measurements. Additionally, we prove an equivalence between quantum statistical queries and quantum differential privacy in the local model, thereby addressing an open question posed by Arunachalam et al. (2021). Finally, we consider the task of quantum multi-party computation under local differential privacy, demonstrating that parity functions can be efficiently learned in this model, whereas the corresponding classical task requires exponentially many samples.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 5","pages":"3675-3692"},"PeriodicalIF":2.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1109/TIT.2025.3552660
Boris Ryabko
Nowadays there are several classes of constrained codes intended for different applications. The following two large classes can be distinguished. The first class contains codes with local constraints; for example, the source data must be encoded by binary sequences containing no sub-words 00 and 111. The second class contains codes with global constraints; for example, the code-words must be binary sequences of certain even length where half of the symbols are zeros and half are ones. It is important to note that often the necessary codes must fulfill some requirements of both classes. In this paper we propose a general polynomial complexity method for constructing codes for both classes, as well as for combinations thereof. The proposed method uses the Cover enumerative code, but calculates all the parameters on the fly with polynomial complexity, unlike the known applications of that code which employ combinatorial formulae. The main idea of the paper is to use dynamic programming to perform calculations like: how many sequences with a given prefix and a given suffix length satisfying constraints exist. For the constraints under consideration, we do not need to know the entire prefix, but much less knowledge about the prefix is sufficient. That is, we only need a brief description of the prefix.
{"title":"A General Method for the Development of Constrained Codes","authors":"Boris Ryabko","doi":"10.1109/TIT.2025.3552660","DOIUrl":"https://doi.org/10.1109/TIT.2025.3552660","url":null,"abstract":"Nowadays there are several classes of constrained codes intended for different applications. The following two large classes can be distinguished. The first class contains codes with local constraints; for example, the source data must be encoded by binary sequences containing no sub-words 00 and 111. The second class contains codes with global constraints; for example, the code-words must be binary sequences of certain even length where half of the symbols are zeros and half are ones. It is important to note that often the necessary codes must fulfill some requirements of both classes. In this paper we propose a general polynomial complexity method for constructing codes for both classes, as well as for combinations thereof. The proposed method uses the Cover enumerative code, but calculates all the parameters on the fly with polynomial complexity, unlike the known applications of that code which employ combinatorial formulae. The main idea of the paper is to use dynamic programming to perform calculations like: how many sequences with a given prefix and a given suffix length satisfying constraints exist. For the constraints under consideration, we do not need to know the entire prefix, but much less knowledge about the prefix is sufficient. That is, we only need a brief description of the prefix.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 5","pages":"3510-3515"},"PeriodicalIF":2.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1109/TIT.2025.3550960
Gao Huang;Song Li
This note demonstrates that we can stably recover rank-r Toeplitz matrix $pmb {X}in mathbb {R}^{ntimes n}$ from a number of rank-one subgaussian measurements on the order of $rlog ^{2} n$ with an exponentially decreasing failure probability by employing a nuclear norm minimization program. Our approach utilizes descent cone analysis through Mendelson’s small ball method with the Toeplitz constraint. The key ingredient is to determine the spectral norm of the random matrix of the Toeplitz structure, which may be of independent interest. This improves upon earlier analyses and resolves the conjecture in Chen et al. (IEEE Transactions on Information Theory, 61(7):4034–4059, 2015).
{"title":"Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix","authors":"Gao Huang;Song Li","doi":"10.1109/TIT.2025.3550960","DOIUrl":"https://doi.org/10.1109/TIT.2025.3550960","url":null,"abstract":"This note demonstrates that we can stably recover rank-<italic>r</i> Toeplitz matrix <inline-formula> <tex-math>$pmb {X}in mathbb {R}^{ntimes n}$ </tex-math></inline-formula> from a number of rank-one subgaussian measurements on the order of <inline-formula> <tex-math>$rlog ^{2} n$ </tex-math></inline-formula> with an exponentially decreasing failure probability by employing a nuclear norm minimization program. Our approach utilizes descent cone analysis through Mendelson’s small ball method with the Toeplitz constraint. The key ingredient is to determine the spectral norm of the random matrix of the Toeplitz structure, which may be of independent interest. This improves upon earlier analyses and resolves the conjecture in Chen et al. (IEEE Transactions on Information Theory, 61(7):4034–4059, 2015).","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 5","pages":"3950-3956"},"PeriodicalIF":2.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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