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Cynthia Dwork

Cynthia Dwork is an American theoretical computer scientist recognized for establishing the mathematical foundations of differential privacy, a framework that quantifies and guarantees individual privacy in data analysis outputs.^[1]^[2] She holds the position of Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences, with affiliations in Harvard Law School and the Department of Statistics.^[3]
Dwork's career includes over three decades in industrial research laboratories at IBM and Microsoft before joining Harvard, during which she advanced cryptography through innovations such as non-malleable encryption, the first lattice-based public-key cryptosystem, and proof-of-work mechanisms that underpin modern cryptocurrencies.^[1] Her work extends to fault-tolerant distributed systems and statistical validity in adaptive data analysis, addressing core challenges in ensuring reliable inferences from explored datasets.^[1] Differential privacy, co-developed by Dwork, has been deployed widely, including in Apple's devices for location services and the U.S. Census Bureau's 2020 disclosure avoidance system.^[1]^[4]
Among her honors, Dwork received the National Medal of Science in 2025 for contributions to privacy, cryptography, and distributed computing; the ACM-IEEE Knuth Prize in 2020; the Gödel Prize; and the ACM Paris Kanellakis Theory and Practice Award in 2021.^[5]^[6]^[2] She is a member of the National Academy of Sciences and the National Academy of Engineering.^[3]

Personal Background

Early Life and Education

Cynthia Dwork was born on June 27, 1958, in the United States, as the daughter of mathematician Bernard Dwork, who served as the Eugene Higgins Professor of Mathematics at Princeton University from 1964 until his retirement.^[7] Her sister, Debórah Dwork, is a historian specializing in Holocaust studies.^[8] Dwork completed her undergraduate studies at Princeton University, earning a Bachelor of Science in Engineering (B.S.E.) in electrical engineering and computer science in 1979; she graduated cum laude and received the Charles Ira Young Award for Excellence in Independent Research, recognizing outstanding senior thesis work.^[9] ^[10] She then pursued graduate studies at Cornell University, obtaining a Ph.D. in computer science in 1983 under the supervision of John Hopcroft; her dissertation, titled Bounds on Fundamental Problems in Parallel and Distributed Computation, addressed theoretical limits in concurrent systems.^[11]

Professional Career

Academic and Research Positions

Following her PhD from Cornell University in 1982, Cynthia Dwork began her research career at IBM Research, serving as a Research Staff Member at the Almaden Research Center from August 1985 to June 2000.^[12] In this role, she contributed to foundational work in distributed computing and cryptography within IBM's industrial research environment. In 2001, Dwork transitioned to Microsoft Research, where she held the position of Distinguished Scientist, primarily at the Silicon Valley laboratory, continuing her research until 2017.^[7] This period marked over a decade of leadership in theoretical computer science at one of the premier corporate research institutions. Dwork joined Harvard University in January 2017 as the Gordon McKay Professor of Computer Science at the John A. Paulson School of Engineering and Applied Sciences.^[13] She concurrently serves as Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study and maintains an affiliation with Microsoft Research.^[14] In recent years, Dwork has engaged in visiting lectureships, including a scheduled series of Messenger Lectures at Cornell University from May 5 to 7, 2025, focused on data privacy.^[11]

Key Collaborations and Influences

Dwork's foundational contributions to distributed computing emerged from collaborations with Nancy Lynch at MIT and Larry Stockmeyer at IBM Almaden Research Center, particularly their 1988 paper introducing partial synchrony as a model bridging asynchronous and synchronous systems for consensus protocols.^[15] This joint effort, which analyzed timing uncertainties in fault-tolerant systems, earned the 2007 Edsger W. Dijkstra Prize for its enduring impact on distributed agreement algorithms.^[10] These partnerships during her early industrial research at IBM highlighted how interactions with systems theorists like Lynch shaped Dwork's approach to resilience in unreliable networks.^[16] In cryptography and privacy, Dwork's long-term collaboration with Moni Naor of the Weizmann Institute produced influential works, including their 1992 proposal for resource pricing via computational puzzles to deter junk mail, laying groundwork for proof-of-work mechanisms.^[17] Their co-authorship continued into privacy, notably the 2006 exploration of disclosure risks in statistical databases, which argued for differential privacy as a robust alternative to weaker anonymization techniques.^[18] Naor's expertise in secure computation complemented Dwork's, fostering innovations like proofs under continual observation.^[19] At Microsoft Research, where Dwork spent over two decades, joint projects with researchers such as Omer Reingold and Guy Rothblum advanced non-malleable cryptography and succinct arguments, integrating complexity-theoretic tools into privacy protocols.^[20] These institutional networks, spanning IBM and Microsoft, influenced her synthesis of distributed systems reliability with cryptographic primitives, distinct from purely academic lineages in complexity theory.^[1] Upon joining Harvard in 2017, Dwork's collaborations extended to interdisciplinary efforts in fairness, building on prior co-authorship patterns without supplanting earlier distributed and crypto foundations.^[3]

Research Contributions

Foundations in Distributed Computing

Cynthia Dwork's foundational contributions to distributed computing in the 1980s centered on achieving consensus and agreement in fault-prone environments, addressing challenges posed by processor failures, timing uncertainties, and adversarial behaviors. Collaborating with Nancy Lynch and Larry Stockmeyer, she developed models and protocols for consensus under partial synchrony, a framework that relaxes strict synchronous assumptions while avoiding the impossibilities of fully asynchronous systems. This work delineated conditions under which reliable agreement is achievable despite bounded delays and faults, providing essential theoretical bounds for system designers.^[15]^[16] In their 1988 Journal of the ACM paper, Dwork, Lynch, and Stockmeyer formalized partial synchrony by introducing parameters for upper bounds on message delays and relative processor speeds, enabling protocols that terminate correctly if the system stabilizes within these bounds. The protocols tolerate up to one-third faulty processors in Byzantine settings, using authenticated messages to prevent invalidation by malicious actors, and achieve both safety (all non-faulty processors agree on the same value) and liveness (non-faulty processors eventually decide). This bridged gaps in prior synchronous models, which assumed fixed rounds, and asynchronous ones, where consensus is impossible even with crashes per the FLP result, offering practical resilience for networks with variable but bounded asynchrony.^[15]^[16] Dwork also advanced fault tolerance in constrained topologies, such as networks of bounded degree. In a 1986 SIAM Journal on Computing paper, she introduced "almost-everywhere agreement," a paradigm where consensus is required only among nearly all correct processors, relaxing total agreement to enable efficiency in sparse graphs prone to partitions or failures. This approach tolerated a constant fraction of faults while minimizing communication overhead, influencing designs for scalable, resilient infrastructures like early multiprocessor systems and precursor protocols to modern consensus mechanisms. Her protocols incorporated randomized elements for efficiency and drew on empirical observations of real network behaviors, such as intermittent delays, to validate theoretical guarantees against practical unreliability.^[21]^[22]

Advances in Cryptography

In the early 1990s, while at IBM Almaden Research Center, Dwork co-authored the foundational paper introducing non-malleable cryptography, a strengthening of semantic security for encryption schemes.^[23] Non-malleability ensures that an adversary, given a ciphertext encrypting some message, cannot produce a valid ciphertext for a related but distinct message, even under chosen-ciphertext attacks; this property addresses vulnerabilities in interactive protocols where ciphertexts may be adaptively generated.^[24] The construction relies on verifiable security proofs grounded in the hardness of decisional Diffie-Hellman assumptions, providing causal guarantees against malleability exploits that could undermine protocols like key exchange or multiparty computation, without relying on unproven black-box simulation paradigms.^[25] Building on this, Dwork and Moni Naor proposed computational proof-of-work mechanisms in 1992 to mitigate denial-of-service attacks and resource abuse, such as junk email. These require clients to solve moderately hard puzzles—demonstrating computational effort proportional to requested service—before accessing resources, with server verification efficient and puzzle generation tunable for difficulty. The approach establishes a pricing model via processing costs, offering provably secure deterrence under standard cryptographic assumptions like the hardness of factoring or discrete logarithms, and has influenced practical systems from anti-spam filters to blockchain consensus.^[26] In the late 1990s and early 2000s, Dwork contributed to zero-knowledge proofs through the development of "zaps," non-interactive witness-indistinguishable arguments with public-coin verification.^[27] Zaps enable two-message protocols where the verifier commits first, facilitating applications in concurrent settings and reducing interaction rounds compared to traditional zero-knowledge proofs, while maintaining soundness via extractability from trapdoor commitments. This work emphasizes first-principles reductions to pseudorandom function security, yielding verifiable protocols resilient to resetting adversaries—key for distributed systems—distinct from privacy-preserving mechanisms by prioritizing computational hiding over indistinguishability of outputs.^[28] Dwork's cryptographic innovations also intersected with pseudorandomness, notably in lattice-based constructions linking worst-case lattice problems to average-case security for public-key encryption and generators.^[25] These derandomization techniques convert deterministic verifiers into pseudorandom ones, supporting efficient protocols with provable indistinguishability from random strings under quantum-resistant assumptions, and enabling secure multiparty computation by providing robust randomness sources without interactive oracles.^[29]

Development of Differential Privacy

Differential privacy emerged as a mathematical framework for quantifying and achieving data privacy guarantees against inference attacks, formalized by Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith in their 2006 paper "Calibrating Noise to Sensitivity in Private Data Analysis," presented at the Theory of Cryptography Conference.^[30] The core definition posits that a randomized mechanism satisfies ε-differential privacy if, for any two neighboring datasets differing by at most one record and any measurable subset of possible outputs, the probability of observing a particular output changes by at most a multiplicative factor of e^ε (typically small, e.g., ε ≈ 0.1–1 for strong privacy).^[31] This indistinguishability criterion ensures that no individual's data can be reliably inferred from query outputs, regardless of auxiliary information available to an adversary, shifting privacy from ad-hoc anonymization to provable bounds on causal influence from single records.^[30] A foundational mechanism introduced in the work is the Laplace mechanism, which perturbs the true output of a numeric query function f by adding independent noise drawn from a Laplace distribution with scale parameter Δf / ε, where Δf is the global sensitivity (maximum change in f from altering one record).^[31] For instance, to privately release the average of a bounded dataset, noise is calibrated such that the mechanism outputs f(D) + Lap(Δf / ε), preserving utility for aggregate statistics while bounding privacy loss.^[32] Complementing this, McSherry and Kunal Talwar developed the exponential mechanism in 2007, enabling private selection among discrete alternatives by sampling outputs with probability proportional to exp(ε u(o, D) / (2Δu)), where u is a utility function and Δu its sensitivity, facilitating tasks like private data release or optimization without relying solely on continuous noise.^[32] These primitives enabled practical deployments balancing privacy and utility, with early adoption in industry following Dwork and McSherry's affiliation with Microsoft Research. Microsoft integrated differential privacy into internal data analysis pipelines, such as SQL Server's private aggregations, achieving ε ≈ 1 with utility losses under 5% for large-scale queries on enterprise datasets.^[30] Google applied it in Chrome's usage histograms starting around 2013, injecting Laplace noise to report aggregate browser metrics while ensuring per-user contribution indistinguishability, with reported accuracy degradation of less than 1% for histograms exceeding 100 bins.^[33] In government, the U.S. Census Bureau adopted differential privacy for the 2020 Decennial Census, using a variant of the Laplace mechanism to add calibrated noise to tabulations, protecting against reconstruction attacks at ε = 5.8 globally (tighter locally), though empirical evaluations showed mean squared errors up to 10% higher in small geographies compared to non-private releases.^[34] These implementations underscored the framework's emphasis on quantifiable trade-offs, where privacy budgets (cumulative ε across queries via composition theorems) constrain sequential releases without assuming trusted data custodians.^[31]

Work on Algorithmic Fairness

In her seminal 2012 paper "Fairness Through Awareness," co-authored with Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel, Cynthia Dwork introduced a formal framework for algorithmic fairness centered on individual-level protections rather than aggregate group statistics.^[35] The approach posits that fairness requires treating similar individuals similarly, formalized through a Lipschitz continuity condition: a classifier D satisfies individual fairness if, for a task-specific distance metric \delta on the input space (representing similarity between individuals), D(x) - D(y) \leq L \cdot \delta(x, y) for some constant L, ensuring bounded differences in outcomes for bounded differences in inputs.^[35] This metric \delta must be predefined based on domain knowledge, such as Earth Mover's Distance for distributions over protected attributes, to enforce consistency without relying on observed sensitive features during classification.^[35] Dwork's formulation explicitly contrasts individual fairness with group-based metrics, such as demographic parity (equal positive prediction rates across groups) or equalized odds (equal true/false positive rates conditional on outcomes).^[36] She argued that group metrics often fail to capture nuanced similarities, potentially enforcing artificial equality that ignores legitimate individual differences, while individual fairness aligns with intuitive notions of non-discrimination by preserving distance-based equity.^[35] Subsequent theoretical work building on her ideas, including impossibility theorems, demonstrates that no non-trivial classifier can simultaneously satisfy individual fairness and certain group fairness criteria unless group distributions are identical, highlighting inherent trade-offs rooted in differing base rates or causal structures between groups. These results underscore that enforcing group parity may violate individual similarity when real-world data reflects causal disparities, such as varying qualification rates across demographics due to non-discriminatory factors like education or behavior. Empirical studies applying Dwork's individual fairness lens reveal practical challenges, where imposing fairness constraints—whether individual or group—typically reduces predictive accuracy by constraining model flexibility to fit data patterns. For instance, in credit scoring datasets like German Credit, enforcing Lipschitz-bounded classifiers or group parity increases error rates by 5-20% compared to unconstrained models, as the constraints prevent exploitation of predictive signals correlated with protected attributes (e.g., income proxies). Similarly, in recidivism prediction on the COMPAS dataset, fairness adjustments under individual metrics degrade AUC scores from 0.70 to below 0.65, reflecting the causal reality that outcome disparities often stem from behavioral differences rather than algorithmic bias. Dwork's emphasis on metric design acknowledges these utility costs, advocating for transparency in \delta to balance fairness against performance, rather than presuming disparities indicate injustice.^[35]

Recognition and Awards

Major Honors and Elections

In 2008, Dwork was elected to the National Academy of Engineering for contributions to the theory of secure distributed computing. She was also elected a Fellow of the American Academy of Arts and Sciences that year.^[37] In 2014, she was elected to the National Academy of Sciences. Dwork received the Gödel Prize in 2017, jointly with Frank McSherry, Kobbi Nissim, and Adam Smith, for their 2006 paper introducing differential privacy.^[38] The Donald E. Knuth Prize was awarded to her in 2020 by the Association for Computing Machinery and IEEE Computer Society.^[6] She was awarded the National Medal of Science in 2024 for foundational contributions to computer science, including secure cryptography and privacy-preserving data analysis; the medal was presented on January 13, 2025.^[39]

Debates and Critiques

Limitations of Differential Privacy

Differential privacy does not provide absolute privacy guarantees, as demonstrated by impossibility results showing that achieving strong semantic security—where outputs reveal no information about any individual—while maintaining non-trivial utility is fundamentally unattainable. In their 2006 work, Cynthia Dwork and Moni Naor proved that for any statistical database supporting queries with positive utility, an adversary can reconstruct the entire database with high probability, violating individual privacy under general conditions of privacy violation and utility notions.^[40] This holds even under broad assumptions, emphasizing that differential privacy represents a pragmatic compromise rather than a perfect shield, where privacy leakage scales with the epsilon parameter but cannot eliminate all risks without rendering data useless.^[41] A core trade-off arises in utility loss for rare events, outliers, and small subgroups, where noise addition obscures signals critical for analysis. For instance, in epidemiological studies of rare diseases, differential privacy's calibrated noise can suppress detections in low-prevalence populations, as the mechanism prioritizes bounding individual influence over preserving aggregate accuracy for sparse data.^[42] Similarly, critiques highlight that differential privacy inadvertently hides outliers by design, potentially masking re-identification risks in datasets where anomalies stand out without noise, yet the added perturbation reduces overall data fidelity for downstream tasks like anomaly detection.^[43] This is exacerbated in small subgroups, where privacy protections amplify relative error, limiting applicability to granular analyses without restricting queries to larger aggregates.^[44] Composability introduces further practical limitations, as sequential queries erode the privacy budget, weakening guarantees multiplicatively and complicating deployment in interactive settings. Advanced composition theorems mitigate some degradation but still impose linear or logarithmic penalties in epsilon, making long query sequences inefficient without advanced techniques like zero-concentrated differential privacy.^[45] Critics, including the "Fool's Gold" analysis, argue that over-reliance on differential privacy fosters a false sense of security, as real-world implementations often relax parameters for usability, undermining the mathematical rigor and potentially concealing auxiliary re-identification channels not captured by the definition.^[46] Empirical implementations reveal noise-induced inaccuracies, particularly in public data releases. The U.S. Census Bureau's adoption of differential privacy for 2020 decennial data, using a privacy budget of epsilon=7.4 for geographic products, resulted in systematic discrepancies, including undercounts in rural areas and non-white populations, with errors up to 10% in small census blocks compared to un-noised tabulations.^[47] These distortions affected redistricting and resource allocation, illustrating how theoretical privacy gains manifest as practical utility deficits in high-stakes, low-margin datasets.^[48] Such cases underscore the need for careful parameter tuning and post-processing, yet highlight persistent challenges in balancing protection against verifiable accuracy losses.

Controversies in Algorithmic Fairness

Dwork's framework of individual fairness, which posits that similar individuals should receive similar decisions based on a task-specific similarity metric, has highlighted tensions with group-based fairness criteria such as demographic parity or equalized odds, where outcomes must be statistically balanced across protected groups regardless of individual traits. These definitions often conflict, as demonstrated by impossibility theorems showing that no non-trivial classifier can simultaneously achieve calibration (predictive accuracy conditional on true outcomes) and balance (equal selection rates or error rates across groups) unless base rates of the outcome are identical across groups—a rare empirical condition. For instance, Kleinberg, Mullainathan, and Raghavan's 2016 analysis proves that satisfying equality of false positive rates and true positive rates alongside calibration requires equal prevalence of the positive outcome in groups, underscoring the absence of a universal fairness solution even in simple binary classification settings. Critics argue that group fairness metrics, by mandating outcome parity, overlook causal factors such as pre-existing group differences in qualifications, interests, or behaviors, which can produce legitimate disparate impacts rather than algorithmic bias.^[49] In hiring contexts, for example, enforcing demographic parity may compel algorithms to downrank higher-qualified candidates from overrepresented groups to balance selections, effectively prioritizing ideological equality of outcomes over merit-based equality of opportunity. This approach assumes interchangeability across groups, yet empirical data reveal persistent variances, such as gender differences in occupational preferences and variance in cognitive abilities, leading to natural disparities in applicant pools for roles like engineering or firefighting without invoking discrimination. Such interventions risk systemic inefficiency, as they treat observed disparities as presumptive evidence of bias while disregarding first-principles causal realism that groups may differ in average suitability due to non-discriminatory factors. Studies confirm trade-offs where fairness constraints degrade overall utility and accuracy; for instance, causal analyses show that imposing group fairness reduces decision-maker utility by distorting predictions away from true underlying distributions, with losses quantified in simulations where parity enforcement lowers true positive rates by up to 20-30% in imbalanced settings. In algorithmic lending and hiring, relaxing fairness metrics to prioritize predictive parity or individual treatment preserves higher profitability and hire quality, as evidenced by models where demographic parity compliance sacrifices rank correlation with outcomes. Proponents of alternative views, emphasizing accuracy and opportunity equality, contend that over-reliance on group metrics—often driven by regulatory pressures—imposes political correctness at the expense of performance, potentially exacerbating inequalities by hiring or promoting less capable individuals, as critiqued in analyses of real-world systems like resume screeners.^[50] These debates, influenced by Dwork's foundational distinctions, reveal that no metric resolves all tensions without empirical trade-offs, favoring approaches grounded in verifiable individual merit over aggregate enforced equity.

Legacy and Impact

Influence on Technology and Policy

Apple incorporated differential privacy into its iOS 10 operating system released on September 13, 2016, applying it to features such as emoji prediction and keyboard learning to enable aggregate analytics while bounding the risk of individual data inference.^[51] This adoption marked an early large-scale deployment of the framework Dwork co-developed in 2006, influencing subsequent privacy engineering practices by demonstrating feasible trade-offs between data utility and protection in consumer devices.^[52] Google similarly integrated differential privacy mechanisms into elements of its Privacy Sandbox initiative, launched in 2020 as a cookie-deprecation alternative for web advertising, aiming to aggregate user signals without exposing personal identifiers; though the broader Sandbox faced adoption challenges and partial phase-out by October 2025, its differential privacy components underscored efforts to operationalize privacy guarantees in browser ecosystems.^[53] In the public sector, the U.S. Census Bureau implemented differential privacy as the core of its 2020 Disclosure Avoidance System, releasing census data on August 12, 2021, with controlled noise addition to safeguard respondent confidentiality amid rising re-identification threats from data linkage.^[34] This policy shift, informed by Dwork's foundational work, prioritized formal privacy over traditional suppression methods but drew empirical critiques for introducing systematic errors, particularly in undercounting small geographic units and minorities, with studies showing up to 10-20% distortions in population counts for certain locales that affected redistricting accuracy under the Voting Rights Act.^[48] Such utility losses highlighted causal tensions between stringent privacy enforcement and reliable public statistics, prompting ongoing refinements like reduced epsilon parameters in post-2020 evaluations.^[54] Dwork's contributions to algorithmic fairness definitions have shaped auditing protocols in sectors like lending and hiring, influencing frameworks such as those proposed in the EU AI Act of 2024, which mandate bias assessments but have sparked debates over enforcement costs potentially exceeding €6 billion annually for compliance in high-risk systems.^[55] These tools promote proactive fairness checks yet risk overreach by imposing vague metrics that may deter innovation, as evidenced by reduced AI deployments in regulated domains due to litigation fears rather than inherent technical flaws.^[56] Overall, differential privacy has driven a paradigm toward privacy-by-design in data systems, fostering verifiable protections in both tech products and policy mandates, though balanced against documented reductions in analytical precision that can undermine downstream applications like epidemiological modeling or equitable resource allocation.^[57]

Selected Publications

Dwork's early work on distributed systems includes the 1988 paper "Consensus in the Presence of Partial Synchrony," co-authored with Nancy Lynch and Larry Stockmeyer and published in the Journal of the ACM.^[15] This paper, which has received over 1,300 citations, earned the Edsger W. Dijkstra Prize in 2007 for its enduring impact on fault-tolerant computing.^[12] A pivotal contribution to differential privacy is the 2006 paper "Calibrating Noise to Sensitivity in Private Data Analysis," co-authored with Frank McSherry, Kobbi Nissim, and Adam Smith, presented at the Theory of Cryptography Conference.^[58] With more than 1,700 citations, it received the TCC Test-of-Time Award in 2016.^[59] In the domain of algorithmic fairness, Dwork co-authored "Fairness Through Awareness" in 2012 with Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel, published in the proceedings of the 3rd Innovations in Theoretical Computer Science Conference.^[36] The paper has amassed over 2,100 citations.^[36]

References

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The differential privacy method likely introduced significant discrepancies for rural and non-white populations into 2020 census tabulations.
[48]
The use of differential privacy for census data and its impact on ...
Oct 6, 2021 · We study the impact of the US Census Bureau's latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral ...Missing: Microsoft | Show results with:Microsoft
[49]
Algorithmic Fairness - Stanford Encyclopedia of Philosophy
Jul 30, 2025 · Dwork, Cynthia, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel, 2012, “Fairness through Awareness”, in Proceedings of the ...
[50]
[PDF] "Un"Fair Machine Learning Algorithms
However, in many cases, ensuring equal treatment leads to disparate impact particularly when there are differences among groups based on demographic classes.
[51]
[PDF] Differential Privacy - Apple
The data-gathering features that use differential privacy are linked to the user setting for Device Analytics. Users are presented with the option of ...
[52]
Learning with Privacy at Scale - Apple Machine Learning Research
Dec 6, 2017 · Differential privacy [2] provides a mathematically rigorous definition of privacy and is one of the strongest guarantees of privacy available.
[53]
Differential privacy semantics for On-Device Personalization
Mar 12, 2025 · This document summarizes the privacy approach for On-Device Personalization (ODP) specifically in the context of differential privacy.
[54]
[PDF] Evaluating the Impact of Differential Privacy Using the Census ...
This new approach, while deemed to be the best way to protect respondents' identity and privacy, will result in a significant trade-off in data accuracy, ...
[55]
How to Force Our Machines to Play Fair | Quanta Magazine
Nov 23, 2016 · The computer scientist Cynthia Dwork takes abstract concepts like privacy and fairness and adapts them into machine code for the algorithmic age.<|separator|>
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Algorithmic fairness: challenges to building an effective regulatory ...
Aug 28, 2025 · Several influential definitions of algorithmic fairness have been developed focusing on the error-rate aspect of a predictive algorithm's output ...
[57]
Advancing Differential Privacy: Where We Are Now and Future ...
Feb 1, 2024 · In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP),
[58]
Calibrating Noise to Sensitivity in Private Data Analysis - SpringerLink
Cynthia Dwork & Frank McSherry. Ben-Gurion University, Israel. Kobbi Nissim ... Calibrating Noise to Sensitivity in Private Data Analysis. In: Halevi, S ...
[59]
TCC Test of Time Award - IACR
Calibrating Noise to Sensitivity in Private Data Analysis by Cynthia Dwork (Microsoft Research), Frank McSherry, Kobbi Nissim (Ben-Gurion University), and Adam ...