[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81645-en":3,"doc-seo-81645-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81645,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Plausible Deniability in Fully Homomorphic Computation","Introduces Plausible Deniability in Fully Homomorphic Computation (PD-FHC), enabling outsourcing of Boolean computations to an untrusted cloud while preserving computational privacy for honest-but-curious providers and plausible deniability under coercion. Defines Deniable Computation Medium (DCM) and Deniable Computation Scheme (DCS) abstractions, then instantiates them with RGB image processing using Fredkin-gate circuits. A single fixed Fredkin wiring evaluates the real circuit at real positions and decoy functions elsewhere; under coercion, users reveal a verifiable decoy while the real function remains hidden. Proves information-theoretic position privacy under matched-marginal conditions for LSB-plane exchangeability across general per-position laws and presents Python benchmarks showing competitive performance with TFHE for Boolean circuits while adding deniability beyond native FHE.","Plausible Deniability in Fully Homomorphic  \nComputation  \nShahzad Ahmad  Stefan Rass  Zahra Seyedi   \narXiv :2605 .0 1985v2 [ cs .CR] 10 Jul 2026  \nAbstract—We introduce Plausible Deniability in Fully Homomorphic Computation (PD-FHC), a framework enabling users to outsource Boolean computations to an untrusted cloud while maintaining both computational privacy against honest-butcurious providers and plausible deniability against coercive adversaries. We define the notion of a Deniable Computation Medium (DCM) and a Deniable Computation Scheme (DCS) as medium-independent abstractions, then instantiate them using RGB images with Fredkin-gate circuits. One real circuit and several decoys share a single fixed Fredkin-gate wiring. Embedded control bits decide what each gate computes at each pixel, so the same wiring evaluates the real function at the real positions and decoy functions elsewhere. The cloud applies this one wiring to every pixel identically, processing all circuits ina single pass. Under coercion, the user reveals a decoy with verifiable results while the real circuit stays hidden. We formalize multi-round coercion games with existence and circuit-discovery advantages. For the image instantiation we prove informationtheoretic position privacy under a matched-marginal condition: when the real, decoy, and fill bits are drawn from a common per-position law and placed at random, the embedded LSB plane is exchangeable, so an honest-but-curious provider gains no advantage over guessing at locating the real positions, for any such law and not only the uniform one. We are explicit that this is a condition Alice enforces, that it is distinct from steganalytic undetectability, and that the latter requires the embedded law to match the declared service’s legitimate-input law. Our Python implementation, benchmarked across circuit sizes (5–302 gates) and image dimensions (1282 to 5122), shows competitive performance with TFHE for Boolean circuits while providing deniability that FHE cannot natively offer.  \nIndex Terms—plausible deniability, homomorphic computation, steganography, fredkin gate, cloud computing, privacypreserving computation  \nI. INTRODUCTION  \nOUTSOURCING computation to cloud providers exposes  \nsensitive data to untrusted infrastructure. Fully Homomorphic Encryption (FHE) [1] and Trusted Execution Environments (TEEs) [2] protect data confidentiality but fail to address a distinct threat: coercion. A powerful adversary who compels a user to explain her cloud activity will find that FHE ciphertexts confirm the use of encryption, and TEE attestation logs confirm that a specific computation was executed. Neither technology allows the user to credibly deny her true computational intent. This paper introduces Plausible Deniability in Fully Homomorphic Computation (PD-FHC), a framework enabling users to outsource Boolean computations  \nShahzad Ahmad and Stefan Rass are with the LIT Secure and Correct Systems Lab, Johannes Kepler University, Linz, Austria and Zahra Seyediis associated with Computer Science and Engineering Department, Koc¸ University, Istanbul, T¨urkiye (email: [shahzad.ahmad@jku.at](shahzad.ahmad@jku.at), [stefan.rass@jku.at](stefan.rass@jku.at),  \n[zseyedi@ku.edu.tr](zseyedi@ku.edu.tr)).  \nwhile maintaining the ability to deny, under coercion, what they actually computed.  \nA. Motivating Scenario  \nA photo lab routinely submits large batches of images toa cloud service that applies a per-pixel filter as part of its dithering and quality-control pipeline. This activity is entirely ordinary, and the per-pixel changes are visually imperceptible at the level of single-bit adjustments in each channel. A technician needs to privately check whether a confidential 8-bit measurement v exceeds a threshold τ . She must also be able to deny that this specific computation ever occurred if she is later compelled to explain her cloud use. Neither FHE (conspicuous ciphertexts) nor a TEE (an undeniable attestation log","cbCainzBQ31cieAj","https://ap.wps.com/l/cbCainzBQ31cieAj","pdf",2555864,1,20,"English","en",105,"# Introduction\n## Motivating Scenario\n## Requirements and Threat Model","[{\"question\":\"What problem does PD-FHC address compared with standard FHE and TEEs?\",\"answer\":\"FHE and TEEs protect confidentiality, but they do not enable users to deny the true computational intent under coercion. PD-FHC adds plausible deniability so the user can reveal a decoy with verifiable results while hiding the real computation.\"},{\"question\":\"How does PD-FHC achieve deniable outsourcing in the image instantiation?\",\"answer\":\"It uses one fixed Fredkin-gate wiring applied identically to every pixel. Embedded control bits select what each gate computes at each pixel position, so the wiring evaluates the real function at real positions and decoy functions at other positions.\"},{\"question\":\"What is the Deniable Computation Medium (DCM) and Deniable Computation Scheme (DCS)?\",\"answer\":\"They are medium-independent abstractions that formalize how deniable computation can be described and implemented. The paper instantiates these abstractions using RGB images with Fredkin-gate circuits.\"}]",1784175111,50,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"plausible-deniability-in-fully-homomorphic-computation","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/plausible-deniability-in-fully-homomorphic-computation/81645/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does PD-FHC address compared with standard FHE and TEEs?","Question",{"text":74,"@type":75},"FHE and TEEs protect confidentiality, but they do not enable users to deny the true computational intent under coercion. PD-FHC adds plausible deniability so the user can reveal a decoy with verifiable results while hiding the real computation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does PD-FHC achieve deniable outsourcing in the image instantiation?",{"text":79,"@type":75},"It uses one fixed Fredkin-gate wiring applied identically to every pixel. Embedded control bits select what each gate computes at each pixel position, so the wiring evaluates the real function at real positions and decoy functions at other positions.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the Deniable Computation Medium (DCM) and Deniable Computation Scheme (DCS)?",{"text":83,"@type":75},"They are medium-independent abstractions that formalize how deniable computation can be described and implemented. 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