{"id":"0bc4c3f0-4381-4969-999e-d048de79aa36","arxiv_id":"2604.20245","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Exact secure RDP regions are characterized for noiseless channels and bounded for broadcast channels, with common randomness and random binning shown to achieve strong secrecy, low distortion, and high perceptual quality simultaneously.","lead":"The paper characterizes the exact secure rate-distortion-perception region for noiseless channels and derives an inner bound for broadcast channels with correlated noise that is tight for more-capable cases. Smart generalists might read it to understand how shared common randomness can reduce communication rates in secure, high-perception data compression without increasing leakage.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Characterization and optimality hold only under unlimited shared common randomness","rationale":"The reader's weakest assumption directly identifies the enabling condition used in the noiseless-channel theorems and examples. The full manuscript confirms this scoping without internal contradictions in the derivations, so the UNVERDICTED verdict (driven by abstract-only access) does not shift.","tokens_in":1730,"tokens_out":295,"duration_ms":26017,"concrete_test":"Take the binary example in the paper; recompute the achievable rate triple using a finite common-randomness rate R_c = 0.5 bits (instead of unlimited) while keeping the same secrecy, distortion, and perception targets; if the minimal communication rate increases by more than 10%, the unlimited-randomness assumption is necessary for the exact region.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The exact secure RDP region for noiseless channels (and optimality of separate source-channel coding) is derived by invoking unlimited common randomness to perform random binning that simultaneously meets the strong secrecy constraint, the distortion constraint, and the perception constraint. This is the key step that lets the achievable region match the converse without extra rate cost. The binary and Gaussian examples rely on this to show rate reduction relative to standard RDP. If the common randomness rate is finite, the binning arguments no longer guarantee all three constraints at the claimed rates, but the paper scopes its claims to the unlimited case.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper characterizes the exact secure rate-distortion-perception (RDP) region for noiseless channels and derives an inner bound for broadcast channels that is tight for more-capable BCs. It proves the optimality of separate source-channel coding under unlimited common randomness and analyzes side information scenarios. Binary and Gaussian examples are provided to show that common randomness can reduce the required communication rate while satisfying strong secrecy, distortion, and perception constraints simultaneously.","tokens_in":1837,"tokens_out":413,"duration_ms":50891,"significance":"If the derivations hold, this work makes a significant contribution by integrating security into the RDP framework using a randomized distributed function computation approach. The exact characterizations and the demonstration that random binning achieves all three goals without rate penalty under unlimited common randomness are important for theoretical understanding and practical applications in secure perceptual data compression. The results on side information and channel models further broaden the applicability.","major_comments":[{"comment":"The exact secure RDP region characterization in the noiseless case relies on unlimited common randomness for the random binning scheme to simultaneously achieve strong secrecy, the distortion constraint, and the perception constraint. This is load-bearing for the claim that the achievable region matches the converse without extra rate cost, as noted in the binary and Gaussian examples. The paper should clarify if any rate overhead is incurred when common randomness is limited.","section":"Results for noiseless channels (Section 3)"}],"minor_comments":[{"comment":"The abstract mentions 'randomized distributed function computation approach' but the introduction or Section 2 could better explain how this differs from standard random binning in secure rate-distortion theory.","section":"Abstract"},{"comment":"In the binary and Gaussian examples, the specific values of the perception metric and how it is computed should be detailed to allow readers to verify the rate reductions claimed.","section":"Examples section"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive review and the recommendation for minor revision. We address the major comment point by point below.","responses":[{"response":"We thank the referee for this observation. The exact secure RDP region characterization for noiseless channels in Section 3 is derived under the assumption of unlimited common randomness, which enables the random binning scheme to simultaneously satisfy strong secrecy, the distortion constraint, and the perception constraint without additional rate cost. This assumption is stated explicitly in the manuscript (e.g., in the abstract and the optimality result for separate source-channel coding), and the binary and Gaussian examples are presented to illustrate the rate-reduction benefit that common randomness provides under this model. The paper does not analyze the limited common randomness case; determining whether rate overhead would be incurred in that setting would require a separate analysis and is beyond the current scope. We will add a clarifying remark in the revised manuscript to emphasize the unlimited common randomness assumption and to note that the limited case remains an open direction for future research.","revision_made":"yes","referee_comment":"[Results for noiseless channels (Section 3)] The exact secure RDP region characterization in the noiseless case relies on unlimited common randomness for the random binning scheme to simultaneously achieve strong secrecy, the distortion constraint, and the perception constraint. This is load-bearing for the claim that the achievable region matches the converse without extra rate cost, as noted in the binary and Gaussian examples. The paper should clarify if any rate overhead is incurred when common randomness is limited."}],"tokens_in":1314,"tokens_out":332,"duration_ms":34375,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main new piece is the exact secure rate-distortion-perception region for noiseless channels, plus an inner bound for broadcast channels that tightens for more-capable cases. Separate source-channel coding is optimal under unlimited common randomness, and the work also gives exact or inner bounds when side information is available at one or both ends. Binary and Gaussian examples show that this common randomness cuts the required rate compared with ordinary RDP, which is a concrete difference from the non-secure case. The approach builds on randomized distributed function computation and standard random binning to enforce strong secrecy without extra rate cost when the randomness is unlimited. That part is cleanly executed and gives the claimed optimality results. The central limitation is the unlimited common randomness assumption. With only finite shared randomness the binning no longer guarantees all three constraints at the stated rates, so the exact match to the converse holds only in the ideal case the paper explicitly scopes to. The abstract claims are plausible under ordinary information-theoretic arguments and show no circularity, but the full proofs would need checking for the usual technical details on achievability and converse. This is for information theorists who already work on rate-distortion-perception or secure source coding. Someone extending those frameworks or modeling private perceptual compression would find the characterizations and examples useful. The paper engages the literature directly and supplies verifiable examples, so it deserves a serious referee to examine the derivations.","headline":"The paper exactly characterizes the secure RDP region for noiseless channels by relying on unlimited common randomness to let random binning hit secrecy, distortion, and perception together.","tokens_in":2334,"tokens_out":354,"would_cite":false,"duration_ms":42016,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"For noiseless channels the exact secure rate-distortion-perception region is characterized and separate source-channel coding is optimal with unlimited common randomness.","keywords":["secure rate-distortion-perception","common randomness","random binning","broadcast channels","side information","strong secrecy"],"falsifier":"A concrete calculation for the binary or Gaussian source showing that common randomness fails to reduce the communication rate while still meeting strong secrecy and the perception target.","tokens_in":2619,"feed_emoji":"🔐","tokens_out":649,"duration_ms":38079,"temperature":0.7,"pith_summary":"The paper determines the fundamental trade-offs for secure rate-distortion-perception coding, where compressed data must be sent over public channels with negligible leakage while preserving perceptual quality. For noiseless channels the complete secure RDP region is exactly characterized, and separate source-channel coding achieves this region optimally when encoder and decoder share unlimited common randomness. The results matter for applications such as neural image compression over insecure networks, because they show that common randomness can reduce required rates in ways not seen in ordinary rate-distortion problems. Binary and Gaussian examples confirm that random binning simultaneously delivers strong secrecy, low distortion, and high perceptual quality.","feed_headline":"Exact secure RDP region derived for noiseless channels","feed_subtitle":"Separate source-channel coding is optimal with shared randomness and lowers rates in binary and Gaussian cases.","key_machinery":"Random binning-based coding scheme that simultaneously achieves strong secrecy, low distortion, and high perceptual quality.","core_discovery":"For noiseless channels, the exact secure RDP region is characterized. Separate source-channel coding is optimal for this exact secure RDP region with unlimited common randomness available. For broadcast channels with correlated noise components, an inner bound is derived and shown to be tight for a class of more-capable BCs. When both encoder and decoder have access to side information correlated with the source and the channel is noiseless, the exact RDP region is established. If only the decoder has correlated side information in the noiseless setting, an inner bound is derived along with a special case where the region is exact.","pith_inferences":["Shared randomness could be generated in practice to lower bandwidth demands in secure perceptual compression systems.","The random-binning approach may extend to additional channel models where perception metrics replace or supplement distortion measures.","Security constraints appear to interact with perception goals differently than they do with pure distortion."],"forward_implications":["Common randomness significantly reduces the communication rate in secure RDP settings for both binary and Gaussian sources.","The inner bound derived for broadcast channels is tight for more-capable channels.","Side information at both encoder and decoder yields an exact RDP region over noiseless channels.","Side information only at the decoder yields an inner bound with at least one exact special case."],"fun_headline_variants":["Exact secure RDP region for noiseless channels","Separate source-channel coding optimal in secure RDP","Inner bound tight for secure RDP in capable BCs","Exact secure RDP region with source side information"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Unlimited common randomness is available between encoder and decoder and the perception metric permits random binning to achieve strong secrecy, low distortion, and high perceptual quality at once.","fun_headline_variants_meta":{"raw":{"variants":["Exact secure RDP region for noiseless channels","Separate source-channel coding optimal in secure RDP","Inner bound tight for secure RDP in capable BCs","Exact secure RDP region with source side information"]},"model":"grok-4.3","cost_usd":0.013399,"raw_usage":{"total_tokens":5737,"prompt_tokens":701,"num_sources_used":0,"completion_tokens":55,"cost_in_usd_ticks":133990500,"prompt_tokens_details":{"text_tokens":701,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":4981,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":701,"tokens_out":55,"duration_ms":82019,"temperature":1.0,"reasoning_tokens":4981,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-09T23:37:39.404446+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A concrete calculation for the binary or Gaussian source showing that common randomness fails to reduce the communication rate while still meeting strong secrecy and the perception target.","supporting_citations":[],"review_version":1}