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1. Reliable Biodiversity Dataset References, https://doi.org/10.17605/OSF.IO/FTZ9B

   Elliott, M. J. (February 2020, iDigBio Communications Luncheon)

   null (Ed.)

   No systematic approach has yet been adopted to reliably reference and provide access to digital biodiversity datasets. Based on accumulated evidence, we argue that location-based identifiers such as URLs are not sufficient to ensure long-term data access. We introduce a method that uses dedicated data observatories to evaluate long-term URL reliability. From March 2019 through May 2020, we took periodic inventories of the data provided to major biodiversity aggregators, including GBIF, iDigBio, DataONE, and BHL by accessing the URL-based dataset references from which the aggregators retrieve data. Over the period of observation, we found that, for the URL-based dataset references available in each of the aggregators' data provider registries, 5% to 70% of URLs were intermittently or consistently unresponsive, 0% to 66% produced unstable content, and 20% to 75% became either unresponsive or unstable. We propose the use of cryptographic hashing to generate content-based identifiers that can reliably reference datasets. We show that content-based identifiers facilitate decentralized archival and reliable distribution of biodiversity datasets to enable long-term accessibility of the referenced datasets. 

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2. Signing data citations enables data verification and citation persistence

   https://doi.org/10.1038/s41597-023-02230-y  

   Elliott, Michael J.; Poelen, Jorrit H.; Fortes, José A. (December 2023, Scientific Data)

   Abstract Commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to content drift, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. We show that content signatures enable independent verification of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe applications of signed citations to solve real-world data collection, identification, and citation challenges. 

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3. Signed Citations: Making Persistent and Verifiable Citations of Digital Scientific Content

   https://doi.org/10.31222/osf.io/wycjn  

   Elliott, Michael John; Poelen, Jorrit H; Fortes, Jose (August 2022, NSF Public Access Repository (NSF-PAR))

   Commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to “content drift”, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. We show that content signatures enable independent verification of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe real-world applications of signed citations used to cite and compile distributed data collections, cite specific versions of existing data networks, and stabilize associations between URLs and content. 

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4. Cryptographic Function Detection in Obfuscated Binaries via Bit-precise Symbolic Loop Mapping,

   Xu, Dongpeng; Ming, Jiang; Wu, Dinghao (May 2017, Proceedings of the 38th IEEE Symposium on Security and Privacy)

   Cryptographic functions have been commonly abused by malware developers to hide malicious behaviors, disguise destructive payloads, and bypass network-based fire- walls. Now-infamous crypto-ransomware even encrypts victim’s computer documents until a ransom is paid. Therefore, de- tecting cryptographic functions in binary code is an appealing approach to complement existing malware defense and forensics. However, pervasive control and data obfuscation schemes make cryptographic function identification a challenging work. Existing detection methods are either brittle to work on obfuscated binaries or ad hoc in that they can only identify specific cryp- tographic functions. In this paper, we propose a novel technique called bit-precise symbolic loop mapping to identify cryptographic functions in obfuscated binary code. Our trace-based approach captures the semantics of possible cryptographic algorithms with bit-precise symbolic execution in a loop. Then we perform guided fuzzing to efficiently match boolean formulas with known reference implementations. We have developed a prototype called CryptoHunt and evaluated it with a set of obfuscated synthetic examples, well-known cryptographic libraries, and malware. Compared with the existing tools, CryptoHunt is a general approach to detecting commonly used cryptographic functions such as TEA, AES, RC4, MD5, and RSA under different control and data obfuscation scheme combinations. 

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5. Time- and Space-Efficient Aggregate Range Queries over Encrypted Databases

   https://doi.org/10.56553/popets-2022-0128  

   Espiritu, Zachary; Markatou, Evangelia Anna; Tamassia, Roberto (October 2022, Proceedings on Privacy Enhancing Technologies)

   We present ARQ, a systematic framework for creating cryptographic schemes that handle range aggregate queries (sum, minimum, median, and mode) over encrypted datasets. Our framework does not rely on trusted hardware or specialized cryptographic primitives such as property-preserving or homomorphic encryption. Instead, ARQ unifies structures from the plaintext data management community with existing structured encryption primitives. We prove how such combinations yield efficient (and secure) constructions in the encrypted setting. We also propose a series of domain reduction techniques that can improve the space efficiency of our schemes against sparse datasets at the cost of small leakage. As part of this work, we designed and implemented a new, open-source, encrypted search library called Arca and implemented the ARQ framework using this library in order to evaluate ARQ’s practicality. Our experiments on real-world datasets demonstrate the efficiency of the schemes derived from ARQ in comparison to prior work. 

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