Differential Privacy Sample Clauses

Differential Privacy. Differential privacy has emerged as one of the strongest privacy definitions for statistical data release. It guarantees that if an adversary knows complete information of all the tuples in D except one, the output of a differentially private randomized algorithm should not give the adversary too much additional information about the remaining tuples. We say datasets D and Dj differing in only one tuple if we can obtain Dj by removing or adding only one tuple from D. A formal definition of differential privacy is given as follows: 3.1.1 (s-differential privacy [3]). Let A be a randomized algorithm over two datasets D and Dj differing in only one tuple, and let O be any arbitrary set of possible outputs of A. Algorithm A satisfies s-differential privacy if and only if the following holds: Pr[A(D) ∈ O] ≤ esPr[A(Dj) ∈ O] Intuitively, differential privacy ensures that the released output distribution of A remains nearly the same whether or not an individual tuple is in the dataset. The most common mechanism to achieve differential privacy is the Laplace mech- anism [3] that adds a small amount of independent noise to the output of a numeric function f to fulfill s-differential privacy of releasing f , where the noise is drawn from Laplace distribution with a probability density function Pr[η = x] = 1 e− |x| . A 2b Laplace noise has a variance 2b2 with a magnitude of b. The magnitude b of the noise depends on the concept of sensitivity which is defined as follows. 3.1.2 (Sensitivity [3]). Let f denote a numeric function and the sensi- tivity of f is defined as the maximal L1-norm distance between the outputs of f over the two datasets D and Dj which differs in only one tuple. Formally, ∆f = maxD,Dj ||f (D) − f (Dj)||1. 3.1. PRELIMINARIES 19
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Differential Privacy. Intuitively, a randomized mechanism A is differ- entially private if its outcome is not significantly affected by the removal or addi- tion of any record. s-differential privacy is formally defined as Pr[A(D) ∈ O] ≤ esPr[A(Dj) ∈ O], where O is any arbitrary set of possible outputs of A, D and Dj are two neighbouring datasets differing in at most one record (i.e. D can be obtained from Dj by adding or removing at most one record). In our problem definition, an adversary should learn approximately the same information about any individual user given D˜ , irrespective of its presence or absence in D, and one individual can be present in up to N snapshots in D. Two series of dynamic datasets D and Dˆ are user-level neighbors if one can be obtained by adding or removing one individual (including all its occurrences in the snapshots) from the other. Then user-level s-differential privacy is defined as below. 4.1. PRELIMINARIES 47 Definition 4.1.1 (user-level s-differential privacy). Let A be a randomized mecha- nism over two user-level neighbors D, and Dˆ which differ in one user’s presence in the entire series, and let O be any arbitrary set of possible outputs of A. Algorithm A satisfies s-differential privacy iff the following holds Pr[A(D) ∈ O] ≤ esPr[A(Dˆ ) ∈ O] Laplace Mechanism. Dwork et al. [3] show that s-differential privacy can be achieved by adding i.i.d. Laplace noise to query result q(D), where D is a dataset. Formally, q˜(D) = q(D) + (ν1, . . . , νM )j, where νi ∼ Lap(0, GS(q)), for i = 1, . . . , M , and M is the dimension of q(D). νi follows a Laplace distribution with mean zero and scale GS(q), where GS(q) denotes the global sensitivity [3] of the query q. The global sensitivity is the maximum L1 distance between the results of q from any two neighbouring datasets D and Dj, formally defined as GS(q) = maxD,Dj ||q(D) − q(Dj)||1. In our problem setting, the global sensitivity of any two user-level neighbors D and Dˆ is formally defined as GS(q) = maxD,Dˆ ||q(D) − q(Dˆ)||N . For a sequence of DP mechanisms, the sequential composition theorem [6] guar- antees its overall privacy as follows: Theorem 4.1.1 (Sequential Composition [6]). For a sequence of n mechanisms M1, . . . , Mn and each Mi provides si-differential privacy, the sequence of Mi will pro- i=1 vide (Σn si) differential privacy. Hence, one way to achieve epsilon-differential privacy for the entire series of D is to apply Laplace mechanism for each Di with noise Lap(N ), which leads to O...
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Related to Differential Privacy

  • Differential Pay Section 1( A). Geographic Area Pay. Classifications C4115, C4116, C4207, C4209, C4211, C4213, C4215, C4221, C4223, C4225: Prevailing basic rates in specific geographical areas for employment of limited duration less than one hundred twenty (120) days will be approved. Employees paid at such rates will not be eligible for vacation, sick leave or holiday benefits. Such rates will be paid only for construction work.

  • Differential Differential is a salary allowance in addition to the basic rate or schedule based upon additional skills, responsibilities, hours of employment, or distasteful or hazardous work.

  • Differentials No evening or night differentials will be applicable for a person on flexible scheduling concepts unless some or all of the hours he/she works would have fallen within the differential period of 7:00 p.m. to 7:00 a.m. had the person not been on a flexible scheduling concept.

  • Data Privacy The Employee hereby explicitly and unambiguously consents to the collection, use and transfer, in electronic or other form, of the Employee’s personal data as described in this Agreement by and among, as applicable, the Company and its Affiliates for the exclusive purpose of implementing, administering and managing the Employee’s participation in the Plan. The Employee understands that the Company and its Affiliates may hold certain personal information about the Employee, including, but not limited to, the Employee’s name, home address and telephone number, date of birth, social insurance number or other identification number, salary, nationality, job title, any Shares or directorships held in the Company or any Affiliate, details of all Performance Shares or any other entitlement to Shares awarded, canceled, exercised, vested, unvested or outstanding in the Employee’s favor, for the exclusive purpose of implementing, administering and managing the Plan (“Personal Data”). The Employee understands that Personal Data may be transferred to any third parties assisting in the implementation, administration and management of the Plan, that these recipients may be located in the United States, the Employee’s country, or elsewhere, and that the recipient’s country may have different data privacy laws and protections than the Employee’s country. The Employee authorizes the recipients to receive, possess, use, retain and transfer the Personal Data, in electronic or other form, for the purposes of implementing, administering and managing the Employee’s participation in the Plan, including any requisite transfer of such Personal Data as may be required to a broker or other third party with whom the Employee may elect to deposit any Shares received upon vesting of the Performance Shares. The Employee understands that refusal or withdrawal of the consents herein may affect the Employee’s ability to participate in the Plan or to realize benefits from the Performance Shares. For more information on the consequences of the Employee’s refusal to consent or withdrawal of consent, the Employee understands that he or she may contact his or her local human resources representative.

  • DRUG-FREE WORKPLACE REQUIREMENTS Contractor will comply with the requirements of the Drug-Free Workplace Act of 1990 and will provide a drug-free workplace by taking the following actions: a. Publish a statement notifying employees that unlawful manufacture, distribution, dispensation, possession or use of a controlled substance is prohibited and specifying actions to be taken against employees for violations. b. Establish a Drug-Free Awareness Program to inform employees about: 1) the dangers of drug abuse in the workplace; 2) the person's or organization's policy of maintaining a drug-free workplace; 3) any available counseling, rehabilitation and employee assistance programs; and, 4) penalties that may be imposed upon employees for drug abuse violations. c. Every employee who works on the proposed Agreement will: 1) receive a copy of the company's drug-free workplace policy statement; and, 2) agree to abide by the terms of the company's statement as a condition of employment on the Agreement. Failure to comply with these requirements may result in suspension of payments under the Agreement or termination of the Agreement or both and Contractor may be ineligible for award of any future State agreements if the department determines that any of the following has occurred: the Contractor has made false certification, or violated the certification by failing to carry out the requirements as noted above. (Gov. Code §8350 et seq.)