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Identification (information)
Identification (information)
Identification (information)
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The code "420 001270000 99 9505" uniquely identifies this parcel.

For data storage, identification is the capability to find, retrieve, report, change, or delete specific data without ambiguity. This applies especially to information stored in databases. In database normalation, the process of organizing the fields and tables of a relational database to minimize redundancy and dependency, is the central, defining function of the discipline.[1][page needed]

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Identification (information)

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Identification in information theory, often referred to as identification via channels, is a communication paradigm where the sender encodes a message from a large set into channel inputs, and the receiver's task is to determine whether a specific message of interest was transmitted, rather than reconstructing the full message content. This approach, distinct from classical Shannon communication which focuses on reliable message reconstruction, allows for the identification of a doubly exponential number of messages—up to approximately 22nC2^{2^{nC}} for block length nn and channel capacity C>0C > 0—with error probabilities approaching zero. Introduced by Rudolf Ahlswede and Gunter Dueck in 1989, the framework highlights that positive channel capacity enables infinite identification rates in the sense of 1nlog⁡log⁡∣M∣→C\frac{1}{n} \log \log | \mathcal{M} | \to C, where ∣M∣| \mathcal{M} | is the message set size. In the for a discrete memoryless channel, an identification code consists of encoding functions that to input sequences, and a decoder that outputs "yes" if the received output is consistent with the hypothesized and "no" otherwise. Two types of errors are considered: the first-kind error (false negative), where the decoder incorrectly rejects the true , and the second-kind error (false positive), where it incorrectly accepts a non-sent , both required to vanish asymptotically as n→∞n \to \infty. The identification capacity equals the channel's Shannon capacity CC, but applies to the logarithm of the logarithm of the set size, enabling vastly larger sets than the exponential 2nC2^{nC} of traditional coding. This result holds for both separate decoding (individual tests per ) and simultaneous decoding (joint tests via a single ), with the latter often achieving the same capacity. The theory has been extended to quantum channels, broadcast channels, channels with feedback, and multiple-access settings, revealing similar capacity formulas in many cases, such as CID=CC_{ID} = C for classical-quantum channels. Practical applications include , where identification verifies embedded markers without altering perceptible content; protocols, enabling efficient identity verification over noisy links; and sensor networks, for low-overhead device identification in resource-constrained environments. Recent research continues to explore converses, code constructions, and connections to post-Shannon paradigms, underscoring identification's role in goal-oriented communication beyond mere transmission.

Overview

Definition

Identification via channels is a paradigm in information theory where the goal is not to reconstruct the entire transmitted message, as in classical Shannon communication, but to determine whether a specific message of interest was sent or not. The sender encodes a message mm from a large set M\mathcal{M} into a channel input sequence xnx^n, and the receiver, upon observing the channel output yny^n, decides "yes" if the output is consistent with the hypothesized message m^\hat{m} and "no" otherwise. This setup allows for identifying among a doubly exponential number of messages, up to approximately 22nC2^{2^{nC}}, where nn is the block length and C>0C > 0 is the channel capacity, with error probabilities vanishing as n→∞n \to \infty. Two types of errors are defined: the first-kind error (false negative), where the decoder rejects the true message, and the second-kind error (false positive), where it accepts a non-sent message. Both must approach zero asymptotically. The identification capacity is given by CID=CC_{ID} = C, the Shannon capacity, but applied to the double logarithm: 1nlog⁡log⁡∣M∣→C\frac{1}{n} \log \log |\mathcal{M}| \to C. This enables message sets vastly larger than the exponential 2nC2^{nC} in traditional coding. Codes can use separate decoding, testing each message individually, or simultaneous decoding via a single output, both achieving the same capacity.

Historical Development

The concept of identification via channels was formally introduced by Rudolf Ahlswede and Gerhard Dueck in their 1989 paper, marking a significant departure from Claude Shannon's 1948 framework focused on reliable message transmission. Historical remarks in their work note that ideas related to higher-than-Shannon rates were explored in an unpublished 1970 manuscript by one of the authors, titled "A New Information Theory: Information Transfer at Rates Exponentially Higher than the Shannon Capacity," but the full theory emerged in the late 1980s. Early extensions in the 1990s included works by Toshio Matsui and others on identification with feedback and compound channels. In 1992, Te Sun Han and Sergio Verdú provided further results on identification rates and converses. The theory saw a strong converse proof by Ahlswede in 2006. The paradigm extended to quantum channels starting with Martin Løber's 1999 generalization, defining the classical identification capacity over quantum channels. Andreas Winter proved a strong converse in 2002. More recent developments, such as those by Patrick Hayden and Winter in 2012, explored quantum identification capacities using decoupling techniques, equating them to entanglement-assisted capacities in some cases. Ongoing research as of 2025 continues to investigate converses, code constructions, and applications in post-Shannon communication scenarios.

Core Principles

Uniqueness and Specificity

In identification via channels, is ensured through the of identification codes that allow the receiver to distinguish whether a specific was transmitted among a vast set, without reconstructing the entire . An identification code consists of encoding functions that map messages from a set M\mathcal{M} to channel input sequences xn∈Xnx^n \in \mathcal{X}^n, and decoding sets Di⊆YnD_i \subseteq \mathcal{Y}^n for each ii, such that the channel output yny^n for the sent falls into DiD_i with high probability, while outputs for other messages fall outside with high probability. This pairwise distinguishability prevents , enabling the identification of up to doubly exponentially many messages, approximately 22nC2^{2^{nC}}, where nn is the block length and C>0C > 0 is the . Specificity refers to the precision of the identification test, governed by two error probabilities that must approach zero asymptotically as n→∞n \to \infty. The first-kind error λ1\lambda_1 (false negative) is the probability that the decoder rejects the true message, i.e., P(yn∉Di∣xn=ϕ(i))≤λ1P(y^n \notin D_i | x^n = \phi(i)) \leq \lambda_1, where ϕ\phi is the encoding function. The second-kind error λ2\lambda_2 (false positive) is the probability that the decoder accepts a non-sent message, i.e., P(yn∈Dj∣xn=ϕ(i))≤λ2P(y^n \in D_j | x^n = \phi(i)) \leq \lambda_2 for i≠ji \neq j. These errors balance the trade-off between reliable detection and minimal false alarms, with the identification capacity achieving CID=CC_{ID} = C, meaning 1nlog⁡log⁡∣M∣→C\frac{1}{n} \log \log |\mathcal{M}| \to C. Higher specificity reduces λ1\lambda_1 and λ2\lambda_2 but is constrained by the channel's noise, requiring careful code construction to maintain uniqueness across the exponentially large message space.

Retrieval Mechanisms

Retrieval in identification systems involves decoding mechanisms that test the received channel output to confirm or reject the hypothesis of a specific message's transmission. In the separate decoding approach, the receiver performs individual tests for each hypothesized message, checking if yny^n belongs to the corresponding decoding set DiD_i, which can be inefficient for large ∣M∣|\mathcal{M}| but achieves the full capacity CID=CC_{ID} = C. For a discrete memoryless channel, this relies on the statistical properties of the output distribution Wn(yn∣xn)W^n(y^n | x^n), ensuring that the typical set for the true input overlaps significantly with DiD_i while others do not. Simultaneous decoding, in contrast, uses a single measurement or positive operator-valued measure (POVM) to jointly test all messages, partitioning the output space into subsets where acceptance for message ii occurs if yny^n falls into the union of elements assigned to DiD_i. This method often matches the separate decoding capacity, Csim,ID=CC_{sim,ID} = C, and is more practical for implementation, as it avoids sequential testing. Efficiency is determined by the channel's distinguishability, with hashing-like random coding arguments ensuring low error probabilities under uniform input distributions. When exact identification fails due to channel noise exceeding typical sets, advanced techniques like soft-decoding or feedback can approximate decisions, though the asymptotic regime assumes error vanishing without such aids.

Techniques and Methods

Unique Identifiers

In identification via channels, unique identifiers correspond to the messages in a large set M\mathcal{M}, where each message m∈Mm \in \mathcal{M} is encoded into a channel input sequence xn(m)x^n(m) to enable the receiver to test whether a specific message was sent. The size of M\mathcal{M} can grow doubly exponentially, up to approximately 22nC2^{2^{nC}} for block length nn and capacity C>0C > 0, far exceeding classical coding limits. These identifiers are not inherent attributes but are abstract labels mapped via encoding functions that ensure distinguishability under channel noise, with error probabilities vanishing as n→∞n \to \infty. Identification codes are constructed using techniques like binary constant-weight codes (CWCs), which assign codewords of fixed weight WW in a space of length SS to minimize overlaps while maximizing the number of identifiers NN. For example, a CWC with parameters (S,N,W,K)(S, N, W, K) has minimum distance d=2(W−K)d = 2(W - K), where KK bounds maximum overlaps between codewords. These are concatenated with Reed-Solomon (RS) codes for error correction: an outer RS code [p−1,ko][p-1, k_o] over alphabet size pp, combined with inner codes, achieves rates approaching the identification capacity CID=CC_{ID} = C. Specific constructions include modified prime sequences yielding CWCs like (p2−p,p,p−1,0)(p^2 - p, p, p-1, 0), optimal for low-rate identification when W/S≈1/2W/S \approx 1/2. Two primary types of identification codes are non-simultaneous (separate) and simultaneous. Separate codes allow independent encoding for each identifier without shared structure, potentially achieving higher rates (e.g., CID=2CC_{ID} = 2C in some cases), while simultaneous codes use a unified set of codewords compatible with decoding. Generation occurs algorithmically: for classical channels, random coding arguments suffice for , but explicit constructions rely on combinatorial designs like CWCs to guarantee uniqueness across distributed or multi-user settings. Bounds such as the Johnson bound limit N≤⌊SW⌊(S−1)(W−1)⋯ ⌋⌋N \leq \lfloor S W \lfloor (S-1)(W-1) \cdots \rfloor \rfloor, ensuring . Standards for such codes are emerging in , analogous to error-correcting code protocols, with extensions to quantum channels using completely positive trace-preserving (CPTP) maps for blind encodings or general maps for visible ones, maintaining CID=CC_{ID} = C.

Indexing Strategies

Decoding in identification via channels involves strategies to "index" or test received outputs against hypothesized identifiers, outputting "yes" or "no" with low error rates. Separate decoding performs individual tests for each message using dedicated decoders DiD_i, mapping outputs yny^n to acceptance if consistent with xn(mi)x^n(m_i), ideal for scenarios testing one specific identifier but requiring multiple measurements. This achieves the capacity CID=CC_{ID} = C, where the rate is 1nlog⁡log⁡∣M∣→C\frac{1}{n} \log \log |\mathcal{M}| \to C. Simultaneous decoding uses a single measurement, such as a {Et}\{E_t\} on the output space, partitioning outcomes into subsets Di=∑t∈IiEtD_i = \sum_{t \in \mathcal{I}_i} E_t for joint tests across all identifiers. This structure supports efficient hardware implementation in quantum or multi-access channels, with the same capacity Csim,ID=CC_{sim,ID} = C under mild conditions, though it may require larger codebooks for . For example, in classical-quantum channels, blind codes employ CPTP encodings E:P(K)→S(A)E: \mathcal{P}(K) \to \mathcal{S}(A), tested via operators DϕD_\phi with error ϵ\epsilon. Composite strategies combine identifiers for multi-user or broadcast settings, optimizing joint encodings for multiple receivers; for instance, in multiple-access channels, codes concatenate user-specific CWCs to handle interference while preserving individual identification rates. Inverted-like approaches appear in fingerprinting constructions, mapping identifiers to nearly orthogonal states for rapid subset queries in search-like tasks. Performance trade-offs include : separate decoding scales linearly with ∣M∣|\mathcal{M}| but incurs higher costs, while simultaneous decoding offers logarithmic overhead via POVMs but demands precise partitioning to avoid type-II errors (false positives). Analyses show storage overhead negligible due to doubly exponential growth, with maintenance via random coding ensuring asymptotic optimality.

Applications

Identification via channels has found applications in several areas where the goal is to verify the presence or identity of a specific message or signal over noisy communication links, rather than transmitting full content. These include , protocols, networks, and emerging .

Digital Watermarking

In , identification codes embed imperceptible markers into content, such as images or audio, to verify or authenticity without significantly altering the perceptible quality. The receiver uses an identification decoder to check if a specific was embedded, leveraging the doubly exponential message set size to support vast libraries of unique markers. This approach is particularly useful for protection and content tracking in media distribution platforms. For classical-quantum channels, the identification capacity matches the Shannon capacity, enabling robust watermark detection over .

Authentication Protocols

Authentication protocols benefit from identification via channels by allowing efficient verification of user or device identities over unreliable links, such as networks. Instead of transmitting sensitive full credentials, the sender encodes an identity , and the receiver performs a hypothesis test to confirm or reject it with vanishing error probabilities. This reduces bandwidth and computational overhead compared to traditional encryption-based methods, making it suitable for resource-limited IoT devices. Extensions to channels with feedback further enhance reliability in interactive scenarios.

Sensor Networks

In sensor networks, identification via channels enables low-overhead alert or event identification, where sensors transmit codes indicating specific anomalies (e.g., thresholds) without sending streams. Multiple sensors can use multiple-access identification schemes to identify relevant events collectively, achieving capacities equal to the underlying . This is critical for energy-constrained environments, such as or smart grids, where full reconstruction is unnecessary and inefficient. Recent work has extended this to networks, maintaining similar performance bounds.

Vehicle-to-X Communication

Vehicle-to-X (V2X) systems apply identification via channels for announcing and verifying specific traffic events or intentions, such as braking or changes, affecting nearby . Each encodes its identity and event type into channel inputs, allowing receivers to identify if a relevant announcement was sent without decoding extraneous details. This supports real-time applications in intelligent transportation systems, with broadcast channel extensions handling multi-receiver scenarios. As of 2023, theoretical bounds confirm achievability for discrete memoryless broadcast channels. Research as of 2025 continues to explore these applications, including converse bounds and constructions for practical implementation in post-Shannon communication paradigms.

Challenges and Considerations

Ambiguity and Errors

in identification systems arises primarily from duplicate identifiers, often from poor that lacks robust unique constraints or , leading to multiple records representing the same . For instance, variations in formats, such as abbreviations or typos (e.g., "St." versus "Street"), can generate unintended duplicates when systems fail to normalize inputs effectively. errors exacerbate this issue during system integrations, where disparate sources with incompatible identifier schemes result in overlapping or conflicting records, as seen in healthcare environments where studies have found that 92% of duplicate errors occur during registration processes due to inconsistent , such as misspellings or incorrect dates. These ambiguities undermine the reliability of identification by creating fragmented views of entities, complicating queries and analyses. Common error types include orphaned records and hash collisions, both of which disrupt linkage and uniqueness in identification processes. Orphaned records occur when a foreign key in a child table references a non-existent primary key in the parent table, typically due to deletions or updates in the parent without corresponding actions in the child, violating referential integrity and leaving unlinked data that appears isolated or invalid. In hash-based identification systems, collisions happen when distinct inputs map to the same hash value, making it impossible to uniquely distinguish entities and potentially leading to misidentification, especially as the number of items approaches the hash space size— for example, with 1,000 possible values and more than about 40 items, the probability of collision exceeds 50%. To mitigate these issues, organizations implement validation rules such as primary and constraints to enforce uniqueness and at the database level, preventing duplicates and orphans during insertion or updates. Checksums, embedded in identifiers like numbers via the , provide an additional layer of error detection by verifying against transmission or entry errors. Periodic audits form a critical ongoing , involving systematic reviews of datasets to trace high-risk areas, identify discrepancies like orphan records, and apply corrective actions such as improved controls or data remediation plans. The real-world impacts of such ambiguities are profound, particularly in corporate mergers where identifier mismatches can lead to significant and operational disruptions. In a of a regional center merging with a smaller , duplicate and mismatched identifiers from legacy systems resulted in challenges reconciling over 700,000 records into an , risking loss of critical patient history and requiring extensive probabilistic matching to avoid incomplete profiles. These errors not only inflate storage costs but also compromise , as seen in broader studies where unresolved duplicates during integrations contribute to inaccurate reporting and potential shortfalls.

Security and Privacy

In information systems, identifiers such as sequential or predictable IDs can expose sensitive data through enumeration attacks, where adversaries systematically probe for valid identifiers to infer or access unauthorized . For instance, insecure direct object references (IDOR), as defined by , occur when applications fail to validate user access to objects referenced by modifiable identifiers, allowing attackers to manipulate sequential IDs to retrieve other users' data. This vulnerability has been exploited in real-world scenarios, such as scraping entire user databases by incrementing exposed sequential IDs in endpoints. Privacy regulations like the EU's (GDPR) mandate of personal identifiers to mitigate re-identification risks, where direct or indirect identifiers could be linked back to individuals. Under GDPR Article 4(5), involves processing so that the data subject is no longer attributable without additional information, thereby reducing the likelihood of unauthorized re-identification while maintaining data utility. The (EDPB) emphasizes that pseudonyms must be designed to obscure identities effectively, and re-identification without consent can constitute an offense. To counter these risks, protection techniques include tokenization, which replaces sensitive identifiers with non-sensitive surrogate values that hold no intrinsic meaning and cannot be reversed without a secure mapping system. The PCI Security Standards Council outlines tokenization as a method where the security of the surrogate relies on the infeasibility of deriving the original data, commonly applied to payment identifiers but extensible to general data fields. Additionally, encryption of identifier fields using standards like AES ensures confidentiality, rendering data inaccessible without the proper key even if intercepted. In biometric identification systems, is enhanced through liveness detection mechanisms to prevent spoofing attacks, where fake representations like photos or masks mimic legitimate . Liveness detection verifies physiological or behavioral traits of a live , such as micro-movements or , as standardized in ISO/IEC 30107 for presentation attack detection. For example, passive liveness detection in facial recognition systems analyzes depth and texture to distinguish real users from spoofs, significantly reducing fraud rates in secure access controls.

References

  1. https://ieeexplore.ieee.org/document/42172/
  2. https://arxiv.org/pdf/1212.0494.pdf
  3. https://inspirehep.net/files/57a3462947dd5fdd7bc0dc427f557a8e
  4. https://arxiv.org/abs/2407.04582
  5. https://eprint.iacr.org/2021/876.pdf
  6. https://doi.org/10.1007/s11128-023-04107-w
  7. https://www.cs.purdue.edu/homes/ake/pub/TKDE-0240-0605-1.pdf
  8. https://pmc.ncbi.nlm.nih.gov/articles/PMC2815491/
  9. https://learn.microsoft.com/en-us/sql/relational-databases/tables/primary-and-foreign-key-constraints?view=sql-server-ver16
  10. https://textbook.prob140.org/notebooks-md/1_03_Collisions_in_Hashing.html
  11. https://www.pda.org/pda-letter-portal/home/full-article/four-steps-to-conducting-a-successful-data-integrity-audit
  12. https://pmc.ncbi.nlm.nih.gov/articles/PMC7883361/
  13. https://owasp.org/www-project-web-security-testing-guide/latest/4-Web_Application_Security_Testing/05-Authorization_Testing/04-Testing_for_Insecure_Direct_Object_References
  14. https://www.azion.com/en/blog/enumeration-attacks-exposed-identifiers-security/
  15. https://infosecwriteups.com/how-i-used-sequential-ids-to-download-an-entire-companys-user-database-and-the-joker-helped-2a8dd23127e6
  16. https://www.edpb.europa.eu/system/files/2025-01/edpb_guidelines_202501_pseudonymisation_en.pdf
  17. https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/anonymisation/pseudonymisation/
  18. https://www.pcisecuritystandards.org/documents/Tokenization_Guidelines_Info_Supplement.pdf
  19. https://www.opentext.com/what-is/tokenization
  20. https://www.1kosmos.com/authentication/liveness-detection/
  21. https://microblink.com/resources/blog/liveness-detection-standards/
  22. https://www.aware.com/passive-liveness-detection-is-the-secret-to-stopping-fraud-blog/
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